make phyloseq object (构建phyloseq对象)

# Reading the raw file(原始文件读取)

metadata = read.delim("./data/data.ps/metadata.tsv")
row.names(metadata) = metadata$SampleID
otutab = read.table("./data/data.ps/otutab.txt", header=T, row.names=1, sep="\t", comment.char="", stringsAsFactors = F)
taxonomy = read.table("./data/data.ps/taxonomy.txt", header=T, row.names=1, sep="\t", comment.char="", stringsAsFactors = F)

library(ggtree)
tree = read.tree("./data/data.ps/otus.tree")

library(Biostrings)
rep = readDNAStringSet("./data/data.ps/otus.fa")

# Import phyloseq package(导入phyloseq(ps)R包)

library(phyloseq)

ps = phyloseq(sample_data(metadata),
              otu_table(as.matrix(otutab), taxa_are_rows=TRUE), 
              tax_table(as.matrix(taxonomy)),
              phy_tree(tree),
              refseq(rep)
              )
ps
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:         [ 2432 taxa and 18 samples ]
#> sample_data() Sample Data:       [ 18 samples by 11 sample variables ]
#> tax_table()   Taxonomy Table:    [ 2432 taxa by 7 taxonomic ranks ]
#> phy_tree()    Phylogenetic Tree: [ 2432 tips and 2431 internal nodes ]
#> refseq()      DNAStringSet:      [ 2432 reference sequences ]

saveRDS(ps,"./data/data.ps/make.ps.rds")

Preparing data analysis (数据分析准备)

Import data (ps object) and R packages(导入数据(ps对象),R包)

# BiocManager::install("ggthemes")

library(tidyverse)
library(phyloseq)
#--输入和设定构建好的ps文件#-----
ps0 = base::readRDS("./data/dataNEW/ps_16s.rds")

#-扩增子分析需要导入的R包#-----------

  library(ggClusterNet)
  library(EasyStat)
  library(fs)
  library(ggthemes)
  library(RColorBrewer)#调色板调用包
  library(magrittr)
  library(MicrobiotaProcess)
  library(ggsignif)
  library(ggtree)
  library(ggtreeExtra)
  library(ggstar)
  library(MicrobiotaProcess)
  library(ggnewscale)
  library(grid)

# 建立结构保存一级目录#--------
result_path <- paste("./","/result_and_plot/",sep = "")
fs::dir_create(result_path)
res1path <- paste(result_path,"/Base_diversity_16s",sep = "")
fs::dir_create(res1path)


#-设定两个所用主题


mytheme1 = ggplot2::theme_bw() + ggplot2::theme(
  panel.background=  ggplot2::element_blank(),
  panel.grid=  ggplot2::element_blank(),
  legend.position="right",
  legend.title =  ggplot2::element_blank(),
  legend.background= ggplot2::element_blank(),
  legend.key= ggplot2::element_blank(),
  plot.title =  ggplot2::element_text(vjust = -8.5,hjust = 0.1),
  axis.title.y =  ggplot2::element_text(colour = "black"),
  axis.title.x = ggplot2::element_text(),
  axis.text =  ggplot2::element_text(),
  axis.text.x =  ggplot2::element_text(),
  axis.text.y =  ggplot2::element_text(),
  legend.text =  ggplot2::element_text()
)


mytheme2 = ggplot2::theme_bw() + ggplot2::theme(
  panel.background=  ggplot2::element_blank(),
  panel.grid=  ggplot2::element_blank(),
  legend.position="right",
  
  legend.title =  ggplot2::element_blank(),
  legend.background=  ggplot2::element_blank(),
  legend.key= ggplot2::element_blank(),
  plot.title =  ggplot2::element_text(vjust = -8.5,hjust = 0.1),
  axis.title.y =  ggplot2::element_text(),
  axis.title.x = ggplot2::element_text(),
  axis.text =  ggplot2::element_text(),
  axis.text.x =  ggplot2::element_text(angle = 90),
  axis.text.y =  ggplot2::element_text(),
  legend.text =  ggplot2::element_text()
)


#---扩增子环境布置
colset1 <- RColorBrewer::brewer.pal(9,"Set1")
colset2 <- RColorBrewer::brewer.pal(12,"Paired")
colset3 <- c(RColorBrewer::brewer.pal(11,"Set1"),RColorBrewer::brewer.pal(9,"Pastel1"))
colset4 = colset3


ps = ps0


#--提取有多少个分组#-----------
gnum = phyloseq::sample_data(ps)$Group %>% unique() %>% length()
gnum
#> [1] 3
# 设定排序顺序
axis_order =  phyloseq::sample_data(ps)$Group %>%unique();axis_order
#> [1] "Group1" "Group2" "Group3"

ps = ps0

ps_biost = ggClusterNet::filter_OTU_ps(ps = ps,Top = 500)

#--R 语言做lefse法分析-过滤#----------
ps_Rlefse = ggClusterNet::filter_OTU_ps(ps = ps,Top = 400)


#-------功能预测#----
if (is.null(ps@refseq)) {
  Tax4Fun = FALSE
} else if(!is.null(ps@refseq)){
  Tax4Fun = TRUE
}

ps.t = ps %>% ggClusterNet::filter_OTU_ps(500)

if (Tax4Fun) {
  dir.create("data")
  otu = ps.t %>% 
    # ggClusterNet::filter_OTU_ps(1000) %>%
    ggClusterNet:: vegan_otu() %>%
    t() %>%
    as.data.frame()
  # write.table(otu,"./data/otu.txt",quote = F,row.names = T,col.names = T,sep = "\t")
  rep = ps.t %>% 
    # ggClusterNet::filter_OTU_ps(1000) %>%
    phyloseq::refseq()
  rep
  # library(Biostrings)
  Biostrings::writeXStringSet(rep,"./data/otu.fa")
  ps.t = ps.t
  
  #开头空一格字符保存
  write.table("\t", "./data/otu.txt",append = F, quote = F, eol = "", row.names = F, col.names = F)
  # 保存统计结果,有waring正常
  write.table(otu, "./data/otu.txt", append = T, quote = F, sep="\t", eol = "\n", na = "NA", dec = ".", row.names = T, col.names = T)     

}

#-构建保存路径
otupath = paste(res1path,"/OTU/",sep = "");otupath
#> [1] ".//result_and_plot//Base_diversity_16s/OTU/"
dir.create(otupath)

Output basic information table(基础信息表格输出)




#--基本表格保存#----------
tabpath = paste(otupath,"/report_table/",sep = "")
dir.create(tabpath)
#--raw otu tab
otu = as.data.frame(t(ggClusterNet::vegan_otu(ps0)))
head(otu)
FileName <- paste(tabpath,"/otutab.csv", sep = "")
write.csv(otu,FileName,sep = "")
# tax table
tax = as.data.frame((ggClusterNet::vegan_tax(ps0)))
head(tax)
FileName <- paste(tabpath,"/tax.csv", sep = "")
write.csv(otu,FileName,sep = "")

ps0_rela  = phyloseq::transform_sample_counts(ps0, function(x) x / sum(x) );ps0_rela 
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:         [ 37178 taxa and 18 samples ]
#> sample_data() Sample Data:       [ 18 samples by 2 sample variables ]
#> tax_table()   Taxonomy Table:    [ 37178 taxa by 7 taxonomic ranks ]
#> phy_tree()    Phylogenetic Tree: [ 37178 tips and 37176 internal nodes ]
#> refseq()      DNAStringSet:      [ 37178 reference sequences ]
#--norm otu tab
otu_norm = as.data.frame(t(ggClusterNet::vegan_otu(ps0_rela)))
FileName <- paste(tabpath,"/otutab_norm.csv", sep = "")
write.csv(otu_norm,FileName,sep = "")

otutax <- cbind(as.data.frame(t(ggClusterNet::vegan_otu(ps0_rela))),as.data.frame((ggClusterNet::vegan_tax(ps0_rela))))
FileName <- paste(tabpath,"/otutax_norm.csv", sep = "")
write.csv(otutax,FileName,sep = "")


for (i in 2: length(phyloseq::rank_names(ps0))) {
  psi  <- ggClusterNet::tax_glom_wt(ps = ps0,ranks = phyloseq::rank_names(ps0)[i])
  #--raw otu tab
  otu = as.data.frame(t(ggClusterNet::vegan_otu(psi)))
  FileName <- paste(tabpath,"/otutab",phyloseq::rank_names(ps0)[i],".csv", sep = "")
  write.csv(otu,FileName,sep = "")
  # tax table
  tax = as.data.frame((ggClusterNet::vegan_tax(ps0)))
  FileName <- paste(tabpath,"/tax",phyloseq::rank_names(ps0)[i],".csv", sep = "")
  write.csv(otu,FileName,sep = "")
  
  psi_rela  = phyloseq::transform_sample_counts(psi, function(x) x / sum(x) );psi_rela 
  #--norm otu tab
  otu_norm = as.data.frame(t(ggClusterNet::vegan_otu(psi_rela)))
  FileName <- paste(tabpath,"/otutab_norm",phyloseq::rank_names(psi)[i],".csv", sep = "")
  write.csv(otu_norm,FileName,sep = "")
  
  otutax <- cbind(as.data.frame(t(ggClusterNet::vegan_otu(psi_rela))),as.data.frame((ggClusterNet::vegan_tax(psi_rela))))
  FileName <- paste(tabpath,"/otutax_norm",phyloseq::rank_names(ps0)[i],".csv", sep = "")
  write.csv(otutax,FileName,sep = "")
}

1.Community diversity analysis(1.群落多样性分析)

alpha diversity(alpha多样性)


alppath = paste(otupath,"/alpha/",sep = "")
dir.create(alppath)

#---------多种指标alpha多样性分析加出图-标记显著性
  group = "Group"
# 设定排序顺序
axis_order =  phyloseq::sample_data(ps)$Group %>%unique();axis_order
#> [1] "Group1" "Group2" "Group3"

  samplesize = min(phyloseq::sample_sums(ps))
  if (samplesize == 0) {
    print("0 number sequence of some samples")
    print("median number were used")
    ps11  = phyloseq::rarefy_even_depth(ps,sample.size = samplesize)
  } else{
    ps11  = phyloseq::rarefy_even_depth(ps,sample.size = samplesize)
  }
  
  mapping = phyloseq::sample_data(ps11)
  ps11 = phyloseq::filter_taxa(ps11, function(x) sum(x ) >0 , TRUE); ps11
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:         [ 27637 taxa and 18 samples ]
#> sample_data() Sample Data:       [ 18 samples by 2 sample variables ]
#> tax_table()   Taxonomy Table:    [ 27637 taxa by 7 taxonomic ranks ]
#> phy_tree()    Phylogenetic Tree: [ 27637 tips and 27635 internal nodes ]
#> refseq()      DNAStringSet:      [ 27637 reference sequences ]
  # head(mapping)
  colnames(mapping) = gsub(group,"AA", colnames(mapping))
  
  mapping$Group = mapping$AA
  mapping$Group = as.factor(mapping$Group)
  # mapping$Group 

  count = as.data.frame(t(ggClusterNet::vegan_otu(ps11)))

  alpha=vegan::diversity(count, "shannon")
  
  x = t(count) ##转置,行为样本,列为OTU
  # head(x)

  Shannon = vegan::diversity(x)  ##默认为shannon
  Shannon
#>  sample1 sample10 sample11 sample12 sample13 sample14 sample15 sample16 
#> 7.683445 7.540214 7.466142 7.694121 7.933365 7.931841 7.901167 7.873805 
#> sample17 sample18  sample2  sample3  sample4  sample5  sample6  sample7 
#> 7.926233 7.923863 7.595181 7.687760 7.895519 7.966725 7.599759 7.254086 
#>  sample8  sample9 
#> 7.424512 6.864458
  Inv_Simpson <- vegan::diversity(x, index = "invsimpson")
  Inv_Simpson
#>  sample1 sample10 sample11 sample12 sample13 sample14 sample15 sample16 
#> 653.4143 442.9412 478.1011 454.2344 908.2110 828.0545 878.2087 805.3710 
#> sample17 sample18  sample2  sample3  sample4  sample5  sample6  sample7 
#> 854.2719 871.1567 737.7287 708.5443 907.1260 898.3528 596.7137 383.6056 
#>  sample8  sample9 
#> 186.1661  80.6827
  
  #计算OTU数量
  S <- vegan::specnumber(x);S  ##每个样本物种数。等价于S2 = rowSums(x>0)
#>  sample1 sample10 sample11 sample12 sample13 sample14 sample15 sample16 
#>     7819     7027     6853     8772    10025    10358     9891    10035 
#> sample17 sample18  sample2  sample3  sample4  sample5  sample6  sample7 
#>    10525    10311     5513     6032     8797    10203     6024     4581 
#>  sample8  sample9 
#>     7882     5271
  S2 = rowSums(x>0)
  
  #多样性指标:均匀度Pielou_evenness,Simpson_evenness
  Pielou_evenness <- Shannon/log(S)
  Simpson_evenness <- Inv_Simpson/S
  est <- vegan::estimateR(x)
  est <- vegan::estimateR(x)
  Richness <- est[1, ]
  Chao1 <- est[2, ]
  ACE <- est[4, ]
  report = cbind(Shannon, Inv_Simpson, Pielou_evenness, Simpson_evenness,
                 Richness, Chao1,ACE) 
  head(report)
#>           Shannon Inv_Simpson Pielou_evenness Simpson_evenness Richness
#> sample1  7.683445    653.4143       0.8571148       0.08356750     7819
#> sample10 7.540214    442.9412       0.8512787       0.06303418     7027
#> sample11 7.466142    478.1011       0.8453090       0.06976522     6853
#> sample12 7.694121    454.2344       0.8474336       0.05178230     8772
#> sample13 7.933365    908.2110       0.8611207       0.09059462    10025
#> sample14 7.931841    828.0545       0.8579124       0.07994348    10358
#>              Chao1       ACE
#> sample1   8426.818  8317.225
#> sample10  7427.908  7359.968
#> sample11  7326.264  7223.793
#> sample12  9450.131  9358.131
#> sample13 11019.921 10966.996
#> sample14 11370.823 11377.163
  
  alp = merge(mapping,report , by="row.names",all=F)

index = c("Shannon","Inv_Simpson","Pielou_evenness","Simpson_evenness" ,"Richness" ,"Chao1","ACE" )
#--多种组合alpha分析和差异分析出图
index= alp
# head(index)

#--提取三个代表指标作图
sel = c(match("Shannon",colnames(index)),match("Richness",colnames(index)),match("Pielou_evenness",colnames(index)))
data = cbind(data.frame(ID = 1:length(index$Group),group = index$Group),index[sel])
# head(data)

result = EasyStat::MuiaovMcomper2(data = data,num = c(3:5))

FileName <- paste(alppath,"/alpha_diversity_different_label.csv", sep = "")
write.csv(result,FileName,sep = "")
FileName <- paste(alppath,"/alpha_diversity_index.csv", sep = "")
write.csv(index,FileName,sep = "")

result1 = EasyStat::FacetMuiPlotresultBox(data = data,num = c(3:5),
                                result = result,
                                sig_show ="abc",ncol = 3 )
p1_1 = result1[[1]] + 
  ggplot2::scale_x_discrete(limits = axis_order) + 
  mytheme1 +
  ggplot2::guides(fill = guide_legend(title = NULL)) +
  ggplot2::scale_fill_manual(values = colset1)
p1_1


#如何升级展示-提取数据用小提琴图展示
p1_1 = result1[[2]] %>% ggplot(aes(x=group , y=dd )) + 
  geom_violin(alpha=1, aes(fill=group)) +
  geom_jitter( aes(color = group),position=position_jitter(0.17), size=3, alpha=0.5)+
  labs(x="", y="")+
  facet_wrap(.~name,scales="free_y",ncol  = 3) +
  # theme_classic()+
  geom_text(aes(x=group , y=y ,label=stat)) +
  ggplot2::scale_x_discrete(limits = axis_order) + 
  mytheme1 +
  guides(color=guide_legend(title = NULL),
         shape=guide_legend(title = NULL),
         fill = guide_legend(title = NULL)
         ) +
  ggplot2::scale_fill_manual(values = colset1)
p1_1


res = EasyStat::FacetMuiPlotresultBar(data = data,num = c(3:5),result = result,sig_show ="abc",ncol = 3)
p1_2 = res[[1]]+ scale_x_discrete(limits = axis_order) + guides(color = FALSE) +
  mytheme1+ 
  guides(fill = guide_legend(title = NULL))+
  scale_fill_manual(values = colset1)
p1_2


res = EasyStat::FacetMuiPlotReBoxBar(data = data,num = c(3:5),result = result,sig_show ="abc",ncol = 3)
p1_3 = res[[1]]+ scale_x_discrete(limits = axis_order) + 
  mytheme1 + 
  guides(fill = guide_legend(title = NULL))+
  scale_fill_manual(values = colset1)
p1_3


FileName <- paste(alppath,"Alpha_Facet_box", ".pdf", sep = "")
ggsave(FileName, p1_1, width = ((1 + gnum) * 3), height =4,limitsize = FALSE)

FileName <- paste(alppath,"Alpha_Facet_bar", ".pdf", sep = "")
ggsave(FileName, p1_2, width = ((1 + gnum) * 3), height = 4,limitsize = FALSE)

FileName <- paste(alppath,"Alpha_Facet_boxbar", ".pdf", sep = "")
ggsave(FileName, p1_3, width = ((1 + gnum) * 3), height = 4,limitsize = FALSE)

FileName <- paste(alppath,"Alpha_Facet_box", ".jpg", sep = "")
ggsave(FileName, p1_1, width = ((1 + gnum) * 3), height =4,limitsize = FALSE)

FileName <- paste(alppath,"Alpha_Facet_bar", ".jpg", sep = "")
ggsave(FileName, p1_2, width = ((1 + gnum) * 3), height = 4,limitsize = FALSE)

FileName <- paste(alppath,"Alpha_Facet_boxbar", ".jpg", sep = "")
ggsave(FileName, p1_3, width = ((1 + gnum) * 3), height = 4,limitsize = FALSE)

#--总体差异检测alpha多样性
krusk1 = ggpubr::compare_means( Shannon ~ group, data=data, method = "kruskal.test")
krusk2 = ggpubr::compare_means( Richness ~ group, data=data, method = "kruskal.test")
krusk3 = ggpubr::compare_means( Pielou_evenness ~ group, data=data, method = "kruskal.test")

dat = rbind(krusk1,krusk2,krusk3) %>% as.data.frame()
FileName <- paste(alppath,"/alpha_diversity_index_all_p_Kruskal-Wallis.csv", sep = "")
write.csv(dat,FileName,sep = "")

alpha-rarefaction curve(alpha稀释曲线)


rare <- mean(phyloseq::sample_sums(ps))/10


  method = "Richness"
  group = "Group"
  start = 100
  step = 3000
  
  # library(microbiome)
  phyRare = function(ps = ps,N = 3000){
    
    otb = as.data.frame(t(ggClusterNet::vegan_otu(ps)))
    otb1 = vegan::rrarefy(t(otb), N)
    ps = phyloseq::phyloseq(phyloseq::otu_table(as.matrix(otb1),taxa_are_rows = F),
                            phyloseq::sample_data(ps)
    )
    
    return(ps)
  }
  

#---- 全部指标#----
all = c("observed" , "chao1"  , "diversity_inverse_simpson" , "diversity_gini_simpson",
          "diversity_shannon"   ,   "diversity_fisher"   ,  "diversity_coverage"     ,    "evenness_camargo",
          "evenness_pielou"    ,   "evenness_simpson"       ,    "evenness_evar" ,   "evenness_bulla",
          "dominance_dbp"      ,  "dominance_dmn"        ,      "dominance_absolute"   ,      "dominance_relative",
          "dominance_simpson"      ,    "dominance_core_abundance" ,  "dominance_gini"  ,           "rarity_log_modulo_skewness",
          "rarity_low_abundance"   ,    "rarity_noncore_abundance",  "rarity_rare_abundance")
  
  #--- 运行计算#----
  for (i in seq(start,max(phyloseq::sample_sums(ps)), by = step) ) {
    psRe = phyRare(ps = ps, N = i)
    

    
    if (method == "Richness") {
      count = as.data.frame(t(ggClusterNet::vegan_otu(psRe)))
      # head(count)
      x = t(count) ##转置,行为样本,列为OTU
      est = vegan::estimateR(x)
      index = est[1, ]
    }
    
    if (method %in% c("ACE")) {
      ap_phy = phyloseq::estimate_richness(psRe, measures =method)
      # head(ap_phy)
      index = ap_phy$ACE
    }
    
    if (method %in% all) {
      alp_mic = microbiome::alpha(psRe,index=method)
      # head(alp_mic)
      index = alp_mic[,1]
    }
    
    tab = data.frame(ID = names(phyloseq::sample_sums(psRe)))
    #得到多样性的列
    tab = cbind(tab,i,index)
    # head(tab)
    if (i == start) {
      result = tab
    }
    if (i != start) {
      result = rbind(result,tab)
    }
  }
  

  for (ii in 1:length(phyloseq::sample_sums(ps))) {
    result$i[result$i > phyloseq::sample_sums(ps)[ii][[1]]]
    df_filter= filter(result, ID ==names(phyloseq::sample_sums(ps)[ii]) &i > phyloseq::sample_sums(ps)[ii][[1]])
    result$index
    result$index[result$i>phyloseq::sample_sums(ps)[ii][[1]]]
    a = result$i>phyloseq::sample_sums(ps)[ii][[1]]
    a[a == FALSE] = "a"
    b = result$ID == names(phyloseq::sample_sums(ps)[ii])
    b[b == FALSE] = "b"
    result$index[a== b] = NA
  }
  #----直接给列,添加分组#----
  map = as.data.frame(phyloseq::sample_data(ps))
  result$Group = map$Group
  
  ## 绘制稀释曲线
  main_theme =theme(panel.grid.major=element_blank(),
                    panel.grid.minor=element_blank(),
                    plot.title = element_text(vjust = -8.5,hjust = 0.1),
                    axis.title.y =element_text(size = 7,face = "bold",colour = "black"),
                    axis.title.x =element_text(size = 7,face = "bold",colour = "black"),
                    axis.text = element_text(size = 7,face = "bold"),
                    axis.text.x = element_text(colour = "black",size = 7),
                    axis.text.y = element_text(colour = "black",size = 7),
                    legend.text = element_text(size = 7,face = "bold")
  )
  head(result)
  result = result %>% dplyr::filter(index != "NA")
  result$ID = as.factor(result$ID)

  p = ggplot(data= result,aes(x = i,y = index,group = ID,color = Group)) +
    geom_line() +
    labs(x= "",y=method,title="") +theme_bw()+main_theme
  p

  #---分组求均值和标准误+se#---
  data = result
  groups= dplyr::group_by(data, Group,i)
  data2 = dplyr::summarise(groups , mean(index), sd(index))
  # head(data2)
  colnames(data2) = c(colnames(data2)[1:2],"mean","sd")
  # 按组均值绘图
  p2 = ggplot(data= data2,aes(x = i,y = mean,colour = Group)) +
    geom_line()+
    labs(x= "",y=method,title="") +theme_bw()+main_theme
  # p2
  
  # 组均值+标准差
  p4 = ggplot(data=data2,aes(x = i,y = mean,colour = Group)) +
    geom_errorbar(data = data2,aes(ymin=mean - sd, ymax=mean + sd,colour = Group),alpha = 0.4, width=.1)+labs(x= "",y=method,title="") +theme_bw()+main_theme
  p4

  # return(list(p,table = result,p2,p4))


#--提供单个样本溪稀释曲线的绘制
p2_1 <- p +
  mytheme1 +
  guides(fill = guide_legend(title = NULL))+
  scale_fill_manual(values = colset1)
## 提供数据表格,方便输出
raretab <- result
head(raretab)
#--按照分组展示稀释曲线
p2_2 <- p2 +
  mytheme1 +
  guides(fill = guide_legend(title = NULL))+
  scale_fill_manual(values = colset1)
#--按照分组绘制标准差稀释曲线
p2_3 <- p4 +
  mytheme1 +
  guides(fill = guide_legend(title = NULL))+
  scale_fill_manual(values = colset1)


FileName <- paste(alppath,"Alpha_rare_sample", ".pdf", sep = "")
ggsave(FileName, p2_1, width = 8, height =6)
FileName <- paste(alppath,"Alpha_rare_group", ".pdf", sep = "")
ggsave(FileName, p2_2, width = 8, height =6)
FileName <- paste(alppath,"Alpha_rare_groupwithSD", ".pdf", sep = "")
ggsave(FileName, p2_3, width = 8, height =6)
FileName <- paste(alppath,"Alpha_rare_sample", ".jpg", sep = "")
ggsave(FileName, p2_1, width = 8, height =6)
FileName <- paste(alppath,"Alpha_rare_group", ".jpg", sep = "")
ggsave(FileName, p2_2, width = 8, height =6)
FileName <- paste(alppath,"Alpha_rare_groupwithSD", ".jpg", sep = "")
ggsave(FileName, p2_3, width = 8, height =6)


FileName <- paste(alppath,"/Alpha_rare_data.csv", sep = "")
write.csv(raretab,FileName,sep = "")

beta diversity(beta多样性)


betapath = paste(otupath,"/beta/",sep = "")
dir.create(betapath)

group = "Group"
dist = "bray"
method ="PCoA"
Micromet = "adonis"
pvalue.cutoff = 0.05
pair=TRUE

# 求取相对丰度#----
ps1_rela = phyloseq::transform_sample_counts(ps, function(x) x / sum(x) )


# 排序方法选择#----
#---------如果选用DCA排序
if (method == "DCA") {
  # method = "DCA"
  ordi = phyloseq::ordinate(ps1_rela, method=method, distance=dist)
  #提取样本坐标
  points = ordi$rproj[,1:2]
  colnames(points) = c("x", "y") #命名行名
  #提取特征值
  eig = ordi$evals^2
}

#---------CCA排序#----
if (method == "CCA") {
  # method = "CCA"
  ordi = phyloseq::ordinate(ps1_rela, method=method, distance=dist)
  #样本坐标,这里可选u或者v矩阵
  points = ordi$CA$v[,1:2]
  colnames(points) = c("x", "y") #命名行名
  #提取特征值
  eig = ordi$CA$eig^2
}

#---------RDA排序#----
if (method == "RDA") {
  # method ="RDA"
  ordi = phyloseq::ordinate(ps1_rela, method=method, distance=dist)
  #样本坐标,这里可选u或者v矩阵
  points = ordi$CA$v[,1:2]
  colnames(points) = c("x", "y") #命名行名
  #提取特征值
  eig = ordi$CA$eig
}

#---------DPCoA排序#----
# 不用做了,不选择这种方法了,这种方法运行太慢了

#---------MDS排序#----
if (method == "MDS") {
  # method = "MDS"
  # ordi = ordinate(ps1_rela, method=ord_meths[i], distance=dist)
  ordi = phyloseq::ordinate(ps1_rela, method=method, distance=dist)
  #样本坐标
  points = ordi$vectors[,1:2]
  colnames(points) = c("x", "y") #命名行名
  #提取解释度
  eig = ordi$values[,1]
}

#---------PCoA排序#----
if (method == "PCoA") {
  # method = "PCoA"
  unif = phyloseq::distance(ps1_rela , method=dist, type="samples")
  #这里请记住pcoa函数
  pcoa = stats::cmdscale(unif, k=2, eig=T) # k is dimension, 3 is recommended; eig is eigenvalues
  points = as.data.frame(pcoa$points) # 获得坐标点get coordinate string, format to dataframme
  colnames(points) = c("x", "y") #命名行名
  eig = pcoa$eig
}

#----PCA分析#----

otu_table = as.data.frame(t(ggClusterNet::vegan_otu(ps1_rela )))
# head(otu_table)
# method = "PCA"
if (method == "PCA") {
  otu.pca = stats::prcomp(t(otu_table), scale.default = TRUE)
  #提取坐标
  points = otu.pca$x[,1:2]
  colnames(points) = c("x", "y") #命名行名
  # #提取荷载坐标
  # otu.pca$rotation
  # 提取解释度,这里提供的并不是特征值而是标准差,需要求其平方才是特征值
  eig=otu.pca$sdev
  eig=eig*eig
}

# method = "LDA"
#---------------LDA排序#----
if (method == "LDA") {
  #拟合模型
  # library(MASS)
  data = t(otu_table)
  # head(data)
  data = as.data.frame(data)
  # data$ID = row.names(data)
  data = scale(data, center = TRUE, scale = TRUE)
  dim(data)
  data1 = data[,1:10]
  map = as.data.frame(sample_data(ps1_rela))
  model = MASS::lda(data, map$Group)

  # 提取坐标
  ord_in = model
  axes = c(1:2)
  points = data.frame(predict(ord_in)$x[, axes])
  colnames(points) = c("x", "y") #命名行名
  # 提取解释度
  eig= ord_in$svd^2
}

#---------------NMDS排序#----
# method = "NMDS"

if (method == "NMDS") {
  #---------如果选用NMDS排序
  # i = 5
  # dist = "bray"
  ordi = phyloseq::ordinate(ps1_rela, method=method, distance=dist)
  #样本坐标,
  points = ordi$points[,1:2]
  colnames(points) = c("x", "y") #命名行名
  #提取stress
  stress = ordi$stress
  stress= paste("stress",":",round(stress,2),sep = "")
}


#---------------t-sne排序#----
# method = "t-sne"
if (method == "t-sne") {
  data = t(otu_table)
  # head(data)
  data = as.data.frame(data)
  # data$ID = row.names(data)
  #
  data = scale(data, center = TRUE, scale = TRUE)

  dim(data)
  map = as.data.frame(sample_data(ps1_rela))
  # row.names(map)
  #---------tsne
  # install.packages("Rtsne")
  # library(Rtsne)

  tsne = Rtsne::Rtsne(data,perplexity = 3)

  # 提取坐标
  points = as.data.frame(tsne$Y)
  row.names(points) =  row.names(map)
  colnames(points) = c("x", "y") #命名行名
  stress= NULL
}


#----差异分析#----

#----整体差异分析#----
g = sample_data(ps)$Group %>% unique() %>% length()
n = sample_data(ps)$Group%>% length()
o = n/g

source("./function/MicroTest.R")
source("./function/pairMicroTest.R")



if (o >= 3) {
  title1 = MicroTest(ps = ps1_rela, Micromet = Micromet, dist = dist)
  title1
} else{
  title1 = NULL
}
#> [1] "Adonis:R 0.364 p: 0.001"



if (F) {
  #----两两比较#----
  pairResult = pairMicroTest(ps = ps1_rela, Micromet = Micromet, dist = dist)
  
} else {
  pairResult = "no result"
}


#----绘图#----
map = as.data.frame(phyloseq::sample_data(ps1_rela))
map$Group = as.factor(map$Group)
colbar = length(levels(map$Group))

points = cbind(points, map[match(rownames(points), rownames(map)), ])
# head(points)
points$ID = row.names(points)


if (method %in% c("DCA", "CCA", "RDA",  "MDS", "PCoA","PCA","LDA")) {
  p2 =ggplot(points, aes(x=x, y=y, fill = Group)) +
    geom_point(alpha=.7, size=5, pch = 21) +
    labs(x=paste0(method," 1 (",format(100*eig[1]/sum(eig),digits=4),"%)"),
         y=paste0(method," 2 (",format(100*eig[2]/sum(eig),digits=4),"%)"),
         title=title1) +
    stat_ellipse(linetype=2,level=0.68,aes(group=Group, colour=Group))

  p3 = p2+ggrepel::geom_text_repel(aes(label=points$ID),size = 5)
  p3
}



if (method %in% c("NMDS")) {
  p2 =ggplot(points, aes(x=x, y=y, fill = Group)) +
    geom_point(alpha=.7, size=5, pch = 21) +
    labs(x=paste(method,"1", sep=""),
         y=paste(method,"2",sep=""),
         title=stress)+
    stat_ellipse( linetype = 2,level = 0.65,aes(group  =Group, colour =  Group))
  
  p3 = p2+ggrepel::geom_text_repel( aes(label=points$ID),size=4)
  p3
  if (method %in% c("t-sne")) {
    supp_lab = labs(x=paste(method,"1", sep=""),y=paste(method,"2",sep=""),title=title)
   p2 = p2 + supp_lab
   p3 = p3 + supp_lab
  }
  eig = NULL
}

if (method %in% c("t-sne")) {
  p2 =ggplot(points, aes(x=x, y=y, fill = Group)) +
    geom_point(alpha=.7, size=5, pch = 21) +
    labs(x=paste(method,"1", sep=""),
         y=paste(method,"2",sep=""))+
    stat_ellipse( linetype = 2,level = 0.65,aes(group  =Group, colour =  Group))
  
  p3 = p2+ggrepel::geom_text_repel( aes(label=points$ID),size=4)
  p3
  if (method %in% c("t-sne")) {
    supp_lab = labs(x=paste(method,"1", sep=""),y=paste(method,"2",sep=""),title=title1)
    p2 = p2 + supp_lab
    p3 = p3 + supp_lab
  }
  eig = NULL
}




  p3_1 = p2 + 
    scale_fill_manual(values = colset1)+
    scale_color_manual(values = colset1,guide = F) +
    mytheme1 + 
    theme(legend.position = c(0.2,0.2))
  p3_1

  #带标签图形出图
  p3_2 = p3 +
    scale_fill_manual(values = colset1)+
    scale_color_manual(values = colset1,guide = F) + 
    mytheme1 + 
    theme(legend.position = c(0.2,0.2))
  p3_2

  
  FileName <- paste(betapath,"/a2_",method,"bray.pdf", sep = "")
  ggsave(FileName, p3_1, width = 8, height = 8)
  FileName1 <- paste(betapath,"/a2_",method,"",method,"bray.jpg", sep = "")
  ggsave(FileName1 , p3_1, width = 12, height = 12)
  
  FileName <- paste(betapath,"/a2_",method,"bray_label.pdf", sep = "")
  ggsave(FileName, p3_2, width = 12, height = 12)
  FileName1 <- paste(betapath,"/a2_",method,"bray_label.jpg", sep = "")
  ggsave(FileName1 , p3_2, width = 12, height = 12)
  
  # 提取出图数据
  plotdata = points
  FileName <-  paste(betapath,"/a2_",method,"bray.csv", sep = "")
  write.csv(plotdata,FileName)
  #---------排序-精修图
  plotdata = points
  head(plotdata)
  # 求均值
  cent <- aggregate(cbind(x,y) ~Group, data = plotdata, FUN = mean)
  cent
  # 合并到样本坐标数据中
  segs <- merge(plotdata, setNames(cent, c('Group','oNMDS1','oNMDS2')),
                by = 'Group', sort = FALSE)
  

  library(ggsci)
  p3_3 = p3_1 +geom_segment(data = segs,
                            mapping = aes(xend = oNMDS1, yend = oNMDS2,color = Group),show.legend=F) + # spiders
    geom_point(mapping = aes(x = x, y = y),data = cent, size = 5,pch = 24,color = "black",fill = "yellow") +
    scale_fill_manual(values = colset1)+
    scale_color_manual(values = colset1,guide = F) + 
    mytheme1 + 
    theme(legend.position = c(0.2,0.2))
  p3_3

  
  FileName <- paste(betapath,"/a2_",method,"bray_star.pdf", sep = "")
  ggsave(FileName, p3_3, width = 8, height = 8)
  FileName1 <- paste(betapath,"/a2_",method,"bray_star.jpg", sep = "")
  ggsave(FileName1 , p3_3, width = 8, height = 8)


# map

#提取总体比较
TResult = title1
head(TResult)
#> [1] "Adonis:R 0.364 p: 0.001"


FileName <- paste(betapath,"Total_anosim.csv", sep = "")
write.csv(TResult,FileName)

#---普氏分析#------

map = phyloseq::sample_data(ps)
samegroup = map$Group %>% table() %>% as.data.frame() %>% .$Freq %>% unique() %>% length() == 1


method =  "spearman"
group = "Group"
ncol = 5
nrow = 2

  dist <- 
    ggClusterNet::scale_micro(ps = ps,method = "rela") %>% 
    ggClusterNet::vegan_otu()%>%
    vegan::vegdist(method="bray") %>%
    as.matrix()
  
  map = phyloseq::sample_data(ps)
  gru = map[,group][,1] %>% unlist() %>% as.vector()
  id = combn(unique(gru),2)
  
  R_mantel = c()
  p_mantel = c()
  name = c()
  R_pro <- c()
  p_pro <- c()
  plots = list()

  for (i in 1:dim(id)[2]) {
    
    id_dist <- row.names(map)[gru == id[1,i]]
    dist1 = dist[id_dist,id_dist]
    id_dist <- row.names(map)[gru == id[2,i]]
    id_dist = id_dist[1:nrow(dist1)]
    dist2 = dist[id_dist,id_dist]
    mt <- vegan::mantel(dist1,dist2,method = method)
    R_mantel[i] = mt$statistic
    p_mantel[i] = mt$signif
    
    name[i] = paste(id[1,i],"_VS_",id[2,i],sep = "")
    #--p
    
    mds.s <- vegan::monoMDS(dist1)
    mds.r <- vegan::monoMDS(dist2)
    pro.s.r <- vegan::protest(mds.s,mds.r)
    
    R_pro[i] <- pro.s.r$ss
    p_pro[i] <- pro.s.r$signif
    
    Y <- cbind(data.frame(pro.s.r$Yrot), data.frame(pro.s.r$X))
    X <- data.frame(pro.s.r$rotation)
    Y$ID <- rownames(Y)
    
    
    
    p1 <- ggplot(Y) +
      geom_segment(aes(x = X1, y = X2, xend = (X1 + MDS1)/2, yend = (X2 + MDS2)/2), 
                   arrow = arrow(length = unit(0, 'cm')),
                   color = "#B2182B", size = 1) +
      geom_segment(aes(x = (X1 + MDS1)/2, y = (X2 + MDS2)/2, xend = MDS1, yend = MDS2), 
                   arrow = arrow(length = unit(0, 'cm')),
                   color = "#56B4E9", size = 1) +
      geom_point(aes(X1, X2), fill = "#B2182B", size = 4, shape = 21) +
      geom_point(aes(MDS1, MDS2), fill = "#56B4E9", size = 4, shape = 21) +
      labs(title =  paste(id[1,i],"-",id[2,i]," ","Procrustes analysis:\n    M2 = ",
                                 round(pro.s.r$ss,3),
                                 ", p-value = ",
                                 round(pro.s.r$signif,3),
                                 "\nMantel test:\n    r = ",
                                 round(R_mantel[i],3),
                                 ", p-value =, ",
                                 round(p_mantel[i],3),sep = "") ) + mytheme1
    p1
    
    plots[[i]] = p1
  }
  
  dat = data.frame(name,R_mantel,p_mantel,R_pro,p_pro )
  pp  = ggpubr::ggarrange(plotlist = plots, common.legend = TRUE, legend="right",ncol = ncol,nrow = nrow)


mytheme1 = theme_classic() + theme(
  panel.background=element_blank(),
  panel.grid=element_blank(),
  legend.position="right",
  
  legend.title = element_blank(),
  legend.background=element_blank(),
  legend.key=element_blank(),
  # legend.text= element_text(size=7),
  # text=element_text(),
  # axis.text.x=element_text(angle=45,vjust=1, hjust=1)
  plot.title = element_text(vjust = -8.5,hjust = 0.1),
  axis.title.y =element_text(size = 15,face = "bold",colour = "black"),
  axis.title.x =element_text(size = 15,face = "bold",colour = "black"),
  axis.text = element_text(size = 10,face = "bold"),
  axis.text.x = element_text(colour = "black",size = 10),
  axis.text.y = element_text(colour = "black",size = 10),
  legend.text = element_text(size = 10,face = "bold")
)
  #--门特尔检验-普氏分析

    size = combn(unique(phyloseq::sample_data(ps)$Group),2) %>% dim()
    size
#> [1] 2 3

    data <- dat
    
    p3_7 <- pp +  mytheme1 
    p3_7

    
    FileName <- paste(betapath,"mantel_pro.csv", sep = "")
    write.csv(data,FileName)
    FileName1 <- paste(betapath,"/a2_","Mantel_Pro.pdf", sep = "")
    ggsave(FileName1 , p3_7, width = size[2] *6, height = 6,limitsize = FALSE)
    FileName1 <- paste(betapath,"/a2_","Mantel_Pro.jpg", sep = "")
    ggsave(FileName1 , p3_7, width = size[2] *6, height =6,limitsize = FALSE)

Species Taxonomic Tree(物种分类树)

library(ggstar)
# library(ggtreeExtra)
library(ggtree)
# library(treeio)
# library(ggstar)
library(ggnewscale)
# #-读取进化树
# library(patchwork)
# library(ggClusterNet)
# library(phyloseq)
# library(tidyverse)
#--微生物组进化树功能
Top_micro = 150
remove_rankID = function(taxtab){
  taxtab$Kingdom = gsub("d__","",taxtab$Kingdom)
  taxtab$Kingdom = gsub("k__","",taxtab$Kingdom)
  taxtab$Phylum = gsub("p__","",taxtab$Phylum)
  taxtab$Class = gsub("c__","",taxtab$Class)
  taxtab$Order = gsub("o__","",taxtab$Order)
  taxtab$Family = gsub("f__","",taxtab$Family)
  taxtab$Genus = gsub("g__","",taxtab$Genus)
  taxtab$Species = gsub("s__","",taxtab$Species)
  return(taxtab)
}

  Top = 100

  tax = ps %>% vegan_tax() %>%
    as.data.frame()
  head(tax)
  tax = remove_rankID(tax) %>%as.matrix()
  tax[is.na(tax)] = "Unknown"
  tax[tax == " "] = "Unknown"
  tax_table(ps) = as.matrix(tax)
  
  alltax = ps %>%
    ggClusterNet::filter_OTU_ps(Top) %>%
    ggClusterNet::vegan_tax() %>%
    as.data.frame()
  alltax$OTU = row.names(alltax)
  
  head(alltax)
  
  trda <- MicrobiotaProcess::convert_to_treedata(alltax)
  p0 <- ggtree::ggtree(trda, layout="inward_circular", size=0.2, xlim=c(30,NA))

  # p0$data
  tax = ps %>%
    ggClusterNet::filter_OTU_ps(Top) %>%
    ggClusterNet::vegan_tax() %>%
    as.data.frame()
  
  tippoint = data.frame(OTU = row.names(tax),Taxa = tax$Phylum,Level = "Phylum")
  
  tippoint$OTU = paste("",tippoint$OTU,sep = "")
  tippoint$names <- gsub("","",tippoint$OTU)
  
  
  p0_1 <- ggtree::ggtree(trda, layout="circular", size=0.2, xlim=c(30,NA)) %<+% tippoint
  p0_1

  p0 <- p0 %<+% tippoint
  p0

  a <- tippoint$Taxa %>% unique() %>% length()
  
  b = rep(18,a)
  names(b) = tippoint$Taxa %>% unique()
  
  
  p1 <- p0 +
    geom_tippoint(
      mapping=aes(
        color=Taxa, 
        shape=Level
      ),
      size=1,
      alpha=0.8
    ) +
    scale_color_manual(values=colorRampPalette(RColorBrewer::brewer.pal(9,"Set1"))(a),
                       guide=guide_legend(
                         keywidth=0.5,
                         keyheight=0.5,
                         order=2,
                         override.aes=list(shape= b,
                                           size=2
                         ),
                         na.translate=TRUE
                       )
    ) +
    scale_shape_manual(values=c("Phylum"=20, "Class"=18), guide="none" )
  
  
  p1_1 <- p0_1 +
    geom_tippoint(
      mapping=aes(
        color=Taxa, 
        shape=Level
      ),
      size=1,
      alpha=0.8
    ) +
    scale_color_manual(values=colorRampPalette(RColorBrewer::brewer.pal(9,"Set1"))(a),
                       guide=guide_legend(
                         keywidth=0.5,
                         keyheight=0.5,
                         order=2,
                         override.aes=list(shape= b,
                                           size=2
                         ),
                         na.translate=TRUE
                       )
    ) +
    scale_shape_manual(values=c("Phylum"=20, "Class"=18), guide="none" )
  
  
  #------对OTU之间的关系进行连线
  otu = ps %>%
    ggClusterNet::filter_OTU_ps(Top) %>%
    ggClusterNet::vegan_otu() %>%
    t() %>%
    as.data.frame()
  head(otu)
  pssub = ps %>%
    ggClusterNet::filter_OTU_ps(Top)
  result = ggClusterNet::corMicro (ps = pssub ,N = 0,r.threshold=0.8,p.threshold=0.05,method = "pearson")
  #--提取相关矩阵
  cor = result[[1]]
  diag(cor) = 0
  
  # library(tidyfst)
  linktab = tidyfst::mat_df(cor) %>% 
    dplyr::filter(value != 0) %>%
    dplyr::mutate(direct = ifelse(value> 0, "positive", "nagetive"))
  colnames(linktab) = c("Inhibitor","Sensitive","Interaction","direct")
  head(linktab)
  linktab$Inhibitor = paste("st__",linktab$Inhibitor,sep = "")
  linktab$Sensitive = paste("st__",linktab$Sensitive,sep = "")
  head(linktab)
  
  # p1$data
  p2 <- p1 +
    ggnewscale::new_scale_color() +
    ggtree::geom_taxalink(
      data=linktab,
      mapping=aes(
        taxa1=Inhibitor, 
        taxa2=Sensitive, 
        color=direct
      ),
      alpha=0.6,
      offset=0.1,
      size=0.15,
      ncp=10,
      hratio=1,
      arrow=grid::arrow(length = unit(0.005, "npc"))
    ) +
    scale_colour_manual(values=c("chocolate2", "#3690C0", "#009E73"),
                        guide=guide_legend(
                          keywidth=0.8, keyheight=0.5,
                          order=1, override.aes=list(alpha=1, size=0.5)
                        )
    )
  
  
  
  otu$id = row.names(otu)
  ringdat = otu %>% tidyfst::longer_dt(id)
  ringdat$value = log2(ringdat$value+1)
  ringdat$id = paste("st__",ringdat$id,sep = "")
  head(ringdat)
  num <- ringdat$name %>% unique() %>% length()
  p3 <- p2 +
    geom_fruit(
      data=ringdat,
      geom=geom_star,
      mapping=aes(
        y=id, 
        x=name, 
        size=value, 
        fill= name
      ),
      starshape = 13,
      starstroke = 0,
      offset=-0.9,
      pwidth=0.8,
      grid.params=list(linetype=3)
    ) +
    scale_size_continuous(range=c(0, 2),
                          limits=c(sort(ringdat$value)[2], max(ringdat$value)),
                          breaks=c(1, 2, 3),
                          name=bquote(paste(Log[2],"(",.("Count+1"), ")")),
                          guide=guide_legend(keywidth = 0.4, keyheight = 0.4, order=4, 
                                             override.aes = list(starstroke=0.3))
    ) +
    scale_fill_manual(
      values=colorRampPalette(RColorBrewer::brewer.pal(12,"Spectral"))(num),
      guide=guide_legend(keywidth = 0.4, keyheight = 0.4, order=3)
    )
  
  
  p4 <- p3 +
    geom_tiplab(
      mapping=aes(
        label=names
      ),
      align=TRUE,
      size=2,
      linetype=NA,
      offset=16
    )
  
  
  map = phyloseq::sample_data(pssub)
  
  dat <- pssub %>%
    ggClusterNet::vegan_otu() %>%
    as.data.frame()
  bartab = cbind(dat,data.frame(row.names = row.names(map),ID = row.names(map),Group = map$Group )) %>%
    dplyr::group_by(Group) %>%
    dplyr::summarise_if(is.numeric,mean) %>%
    as.data.frame()%>% 
    tidyfst::longer_dt(Group)
  head(bartab)
  bartab$name = paste("st__",bartab$name,sep = "")
  num = bartab$Group %>% unique() %>%
    length()
  p5 <- p4 +
    ggnewscale::new_scale_fill() +
    geom_fruit(
      data=bartab,
      geom=geom_bar,
      mapping=aes(
        x=value, 
        y=name, 
        fill= Group
      ),
      stat="identity",
      orientation="y",
      offset=0.48,
      pwidth=1.5,
      axis.params=list(
        axis = "x", 
        text.angle = -45, 
        hjust = 0, 
        vjust = 0.5, 
        nbreak = 4
      )
    ) +
    scale_fill_manual(
      name = "Number of interactions",
      values=colorRampPalette(RColorBrewer::brewer.pal(12,"Set3"))(num),
      guide=guide_legend(keywidth=0.5,keyheight=0.5,order=5)
    ) +
    theme(
      legend.background=element_rect(fill=NA),
      legend.title=element_text(size=6.5),
      legend.text=element_text(size=5),
      legend.spacing.y = unit(0.02, "cm"),
      legend.margin=ggplot2::margin(0.1, 0.9, 0.1,-0.9, unit="cm"),
      legend.box.margin=ggplot2::margin(0.1, 0.9, 0.1, -0.9, unit="cm"),
      plot.margin = unit(c(-1.2, -1.2, -1.2, 0.1),"cm")
    )
  
  
  p5_1 <- p1_1 +
    new_scale_fill() +
    geom_fruit(
      data=bartab,
      geom=geom_bar,
      mapping=aes(
        x=value, 
        y=name, 
        fill= Group
      ),
      stat="identity",
      orientation="y",
      offset=0.1,
      pwidth=1.5,
      axis.params=list(
        axis = "x", 
        text.angle = -45, 
        hjust = 0, 
        vjust = 0.5, 
        nbreak = 4
      )
    ) +
    scale_fill_manual(
      name = "Number of interactions",
      values=colorRampPalette(RColorBrewer::brewer.pal(12,"Set3"))(num),
      guide=guide_legend(keywidth=0.5,keyheight=0.5,order=5)
    ) +
    theme(
      legend.background=element_rect(fill=NA),
      legend.title=element_text(size=6.5),
      legend.text=element_text(size=5),
      legend.spacing.y = unit(0.02, "cm"),
      legend.margin=ggplot2::margin(0.1, 0.9, 0.1,-0.9, unit="cm"),
      legend.box.margin=ggplot2::margin(0.1, 0.9, 0.1, -0.9, unit="cm"),
      plot.margin = unit(c(-1.2, -1.2, -1.2, 0.1),"cm")
    )
  
  

library(ggClusterNet)
barpath = paste(otupath,"/phy_tree_micro/",sep = "");print(barpath)
#> [1] ".//result_and_plot//Base_diversity_16s/OTU//phy_tree_micro/"
dir.create(barpath)


p6 = p1_1
p7 = p5_1

detach("package:ggstar")

FileName <- paste(barpath,Top_micro,"phy_tree_micro1", ".pdf", sep = "")
ggsave(FileName, p0, width = 6, height = 6)
FileName <- paste(barpath,Top_micro,"phy_tree_micro2", ".pdf", sep = "")
ggsave(FileName, p1, width = 7, height = 7)
FileName <- paste(barpath,Top_micro,"phy_tree_micro3", ".pdf", sep = "")
ggsave(FileName, p2, width = 7, height = 7)
FileName <- paste(barpath,Top_micro,"phy_tree_micro4", ".pdf", sep = "")
ggsave(FileName, p3, width = 12, height = 12)
FileName <- paste(barpath,Top_micro,"phy_tree_micro5", ".pdf", sep = "")
ggsave(FileName, p4, width = 15, height = 15)
FileName <- paste(barpath,Top_micro,"phy_tree_micro5", ".pdf", sep = "")
ggsave(FileName, p5, width = 18, height = 18)
FileName <- paste(barpath,Top_micro,"phy_tree_micro6", ".pdf", sep = "")
ggsave(FileName, p6, width = 7, height = 7)
FileName <- paste(barpath,Top_micro,"phy_tree_micro7", ".pdf", sep = "")
ggsave(FileName, p7, width = 12, height = 12)

# library(cowplot)
# save_plot(FileName, p2, base_height = 7, base_width =7)

FileName <- paste(barpath,Top_micro,"phy_tree_micro1", ".png", sep = "")
ggsave(FileName, p0, width = 6, height = 6)
FileName <- paste(barpath,Top_micro,"phy_tree_micro2", ".png", sep = "")
ggsave(FileName, p1, width = 7, height = 7)
FileName <- paste(barpath,Top_micro,"phy_tree_micro3", ".png", sep = "")
ggsave(FileName, p2, width = 7, height = 7,dpi = 72)
FileName <- paste(barpath,Top_micro,"phy_tree_micro4", ".png", sep = "")
ggsave(FileName, p3, width = 12, height = 12,dpi = 72)
FileName <- paste(barpath,Top_micro,"phy_tree_micro5", ".png", sep = "")
ggsave(FileName, p4, width = 15, height = 15,dpi = 72)
FileName <- paste(barpath,Top_micro,"phy_tree_micro5", ".png", sep = "")
ggsave(FileName, p5, width = 18, height = 18,dpi = 72)
FileName <- paste(barpath,Top_micro,"phy_tree_micro6", ".png", sep = "")
ggsave(FileName, p6, width = 7, height = 7,dpi = 72)
FileName <- paste(barpath,Top_micro,"phy_tree_micro7", ".png", sep = "")
ggsave(FileName, p7, width = 12, height = 12,dpi = 72)

Species composition-chord diagram(物种组成-和弦图)


Top = 10
rank = 7
path = barpath



ps_rela = phyloseq::transform_sample_counts(ps, function(x) x / sum(x) );ps_rela 
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:         [ 37178 taxa and 18 samples ]
#> sample_data() Sample Data:       [ 18 samples by 2 sample variables ]
#> tax_table()   Taxonomy Table:    [ 37178 taxa by 7 taxonomic ranks ]
#> phy_tree()    Phylogenetic Tree: [ 37178 tips and 37176 internal nodes ]
#> refseq()      DNAStringSet:      [ 37178 reference sequences ]
  
ps_P <- ps_rela %>%
    ggClusterNet::tax_glom_wt( rank = rank) 
ps_P
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:         [ 1074 taxa and 18 samples ]
#> sample_data() Sample Data:       [ 18 samples by 2 sample variables ]
#> tax_table()   Taxonomy Table:    [ 1074 taxa by 7 taxonomic ranks ]
  
otu_P = as.data.frame((ggClusterNet::vegan_otu(ps_P)))
head(otu_P)
  tax_P = as.data.frame(ggClusterNet::vegan_tax(ps_P))
  
  sub_design <- as.data.frame(phyloseq::sample_data(ps_P))
  count2 =   otu_P
  #数据分组
  iris.split <- split(count2,as.factor(sub_design$Group))
  #数据分组计算平均值
  iris.apply <- lapply(iris.split,function(x)colSums(x[]))
  # 组合结果
  iris.combine <- do.call(rbind,iris.apply)
  ven2 = t(iris.combine)
  # head(ven2)
  lev = phyloseq::rank.names(ps)[rank]
 
  Taxonomies <- ps %>%
    ggClusterNet::tax_glom_wt(rank = rank) %>% 
    phyloseq::transform_sample_counts(function(x) {x/sum(x)} )%>% 
    phyloseq::psmelt() %>%
    #filter(Abundance > 0.05) %>%
    dplyr::arrange( !!sym(lev))
  iris_groups<- dplyr::group_by(Taxonomies, !!sym(lev))
  ps0_sum <- dplyr::summarise(iris_groups, mean(Abundance), sd(Abundance))
  ps0_sum[is.na(ps0_sum)] <- 0
  head(ps0_sum)
  colnames(ps0_sum) = c("ID","mean","sd")
  
  
  ps0_sum <- dplyr::arrange(ps0_sum,desc(mean))
  ps0_sum$mean <- ps0_sum$mean *100
  ps0_sum <- as.data.frame(ps0_sum)
  head(ps0_sum)
  top_P = ps0_sum$ID[1:Top];top_P
#>  [1] "Unassigned"                    "Gemmatimonas_aurantiaca"      
#>  [3] "Gaiella_occulta"               "Terrimonas_lutea"             
#>  [5] "Aquicella_siphonis"            "Ralstonia_pickettii"          
#>  [7] "Bacillus_funiculus"            "Pseudolabrys_taiwanensis"     
#>  [9] "Chryseolinea_serpens"          "Pseudoduganella_violaceinigra"
  
  ### 开始进一步合并过滤
  otu_P = as.data.frame(t(otu_P))
  otu_tax = merge(ven2,tax_P,by = "row.names",all = F)
  dim(otu_tax)
#> [1] 1074   11
  otu_tax[,lev] = as.character(otu_tax[,lev])
  otu_tax[,lev][is.na(otu_tax[,lev])] = "others"
  
  i = 1
  for (i in 1:nrow(otu_tax)) {
    if(otu_tax[,lev] [i] %in% top_P){otu_tax[,lev] [i] = otu_tax[,lev] [i]}
    
    else if(!otu_tax[,lev] [i] %in% top_P){otu_tax[,lev] [i] = "others"}
    
  }
  
  otu_tax[,lev] = as.factor(otu_tax[,lev])
  head(otu_tax)
  
  otu_mean = otu_tax[as.character(unique(sub_design$Group))]
  head(otu_mean)
  row.names(otu_mean) = row.names(otu_tax)
  iris.split <- split(otu_mean,as.factor(otu_tax[,lev]))
  #数据分组计算平均值
  iris.apply <- lapply(iris.split,function(x)colSums(x[]))
  # 组合结果
  iris.combine <- do.call(rbind,iris.apply)
  mer_otu_mean = t(iris.combine)
  
  head(mer_otu_mean )
#>        Aquicella_siphonis Bacillus_funiculus Chryseolinea_serpens
#> Group1         0.04289304         0.02092137           0.02726656
#> Group2         0.03112277         0.06143216           0.02757814
#> Group3         0.03513122         0.01170578           0.02365613
#>        Gaiella_occulta Gemmatimonas_aurantiaca    others
#> Group1      0.07121335               0.1801137 0.6736228
#> Group2      0.11836097               0.1482847 0.9360435
#> Group3      0.06280437               0.1374640 0.7640420
#>        Pseudoduganella_violaceinigra Pseudolabrys_taiwanensis
#> Group1                    0.01523503               0.03331843
#> Group2                    0.02901883               0.02958219
#> Group3                    0.01784443               0.02983005
#>        Ralstonia_pickettii Terrimonas_lutea Unassigned
#> Group1          0.05437782       0.03120037   4.849838
#> Group2          0.03491258       0.03582652   4.547838
#> Group3          0.01315802       0.05091932   4.853445
  
  mi_sam = RColorBrewer::brewer.pal(9,"Set1")
  mi_tax = colorRampPalette(RColorBrewer::brewer.pal(9,"Set3"))(length(row.names(mer_otu_mean)))
  
  grid.col = NULL
  #这里设置样品颜色
  grid.col[as.character(unique(sub_design$Group))] = mi_sam
  #设置群落中物种水平颜色
  # grid.col[colnames(mer_otu_mean)] = mi_tax
  grid.col[row.names(mer_otu_mean)] = mi_tax
  
  FileName2 <- paste(path,"/",phyloseq::rank.names(ps)[rank],"_cricle",".pdf", sep = "")
  pdf(FileName2, width = 12, height = 8)
  
  #gap.degree修改间隔,不同小块之间的间隔
  circlize::circos.par(gap.degree = c(rep(2, nrow(mer_otu_mean)-1), 10, rep(2, ncol(mer_otu_mean)-1), 10),
             start.degree = 180)
  circlize::chordDiagram(mer_otu_mean,
               directional = F,
               diffHeight = 0.06,
               grid.col = grid.col, 
               reduce = 0,
               transparency = 0.5, 
               annotationTrack =c("grid", "axis"),
               preAllocateTracks = 2
  )
  
  circlize::circos.track(track.index = 1, panel.fun = function(x, y) {
    circlize::circos.text(circlize::CELL_META$xcenter, circlize::CELL_META$ylim[1], circlize::CELL_META$sector.index,
                facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.5))}, bg.border = NA)# here set bg.border to NA is important
  circlize::circos.clear()
  dev.off()
#> png 
#>   2
  
  grid.col = NULL
  #这里设置样品颜色
  grid.col[as.character(unique(sub_design$Group))] = mi_sam
  #设置群落中物种水平颜色
  # grid.col[colnames(mer_otu_mean)] = mi_tax
  grid.col[row.names(mer_otu_mean)] = mi_tax
  FileName2 <- paste(path,"/",phyloseq::rank.names(ps)[rank],"_cricle",".png", sep = "")
  png(FileName2, res=150, width = 1000, height = 1008)

  #gap.degree修改间隔,不同小块之间的间隔
  circlize::circos.par(gap.degree = c(rep(2, nrow(mer_otu_mean)-1), 10, rep(2, ncol(mer_otu_mean)-1), 10),
             start.degree = 180)
  circlize::chordDiagram(mer_otu_mean,directional = F,
               reduce = 0,
               diffHeight = 0.06,grid.col = grid.col, transparency = 0.5, annotationTrack =c("grid", "axis"),
               preAllocateTracks = 2
  )
  
  circlize::circos.track(track.index = 1, panel.fun = function(x, y) {
    circlize::circos.text(circlize::CELL_META$xcenter, circlize::CELL_META$ylim[1], circlize::CELL_META$sector.index,
                facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.5))}, bg.border = NA) # here set bg.border to NA is important
  circlize::circos.clear()
dev.off()
#> png 
#>   2

Species composition-stack bar diagram(物种组成-堆叠柱状图)

library(ggalluvial)

group  = "Group"
j = "Phylum"
label = TRUE 
sd = FALSE
Top = 10
tran = TRUE

# axis_order = NA


  # psdata = ps %>%
  #   tax_glom(taxrank = j)
  psdata <- ggClusterNet::tax_glom_wt(ps = ps,ranks = j)
  
  # transform to relative abundance
  if (tran == TRUE) {
    psdata = psdata%>%
      phyloseq::transform_sample_counts(function(x) {x/sum(x)} )
  }
  
  otu = phyloseq::otu_table(psdata)
  tax = phyloseq::tax_table(psdata)
  
  for (i in 1:dim(tax)[1]) {
    if (row.names(tax)[i] %in% names(sort(rowSums(otu), decreasing = TRUE)[1:Top])) {
      
      tax[i,j] =tax[i,j]
    } else {
      tax[i,j]= "others"
    }
  }
  phyloseq::tax_table(psdata)= tax
  
  Taxonomies <- psdata %>% # Transform to rel. abundance
    phyloseq::psmelt()

  Taxonomies$Abundance = Taxonomies$Abundance * 100
  # Taxonomies$Abundance = Taxonomies$Abundance /rep
  colnames(Taxonomies) <- gsub(j,"aa",colnames(Taxonomies))
  data = c()
  i = 2
  for (i in 1:length(unique(phyloseq::sample_data(ps)$Group))) {
    a <- as.data.frame(table(phyloseq::sample_data(ps)$Group))[i,1]

    b =  as.data.frame(table(phyloseq::sample_data(ps)$Group))[i,2]

    c <- Taxonomies %>% 
      dplyr::filter(Group == a)
    c$Abundance <- c$Abundance/b
    data = data.frame(Sample =c$Sample,Abundance = c$Abundance,aa =c$aa,Group = c$Group)

    if (i == 1) {
      table = data
    }
    if (i != 1) {
      table = rbind(table,data)
    }

  }

  Taxonomies = table

  #按照分组求均值

  by_cyl <- dplyr::group_by(Taxonomies, aa,Group)

  zhnagxu2 = dplyr::summarise(by_cyl, sum(Abundance), sd(Abundance))

  iris_groups<- dplyr::group_by(Taxonomies, aa)
  cc<- dplyr::summarise(iris_groups, sum(Abundance))
  head(cc)
  colnames(cc)= c("aa","allsum")
  cc<- dplyr::arrange(cc, desc(allsum))
  
  ##使用这个属的因子对下面数据进行排序
  head(zhnagxu2)
  colnames(zhnagxu2) <- c("aa","group","Abundance","sd")
  zhnagxu2$aa = factor(zhnagxu2$aa,order = TRUE,levels = cc$aa)

  zhnagxu3 = zhnagxu2
  
  ##制作标签坐标,标签位于顶端
  # Taxonomies_x = ddply(zhnagxu3,"group", summarize, label_y = cumsum(Abundance))
  # head(Taxonomies_x )
  #标签位于中部
  # Taxonomies_x1 = ddply(zhnagxu3,"group", transform, label_y = cumsum(Abundance) - 0.5*Abundance)
  Taxonomies_x = plyr::ddply(zhnagxu3,"group", summarize,label_sd = cumsum(Abundance),label_y = cumsum(Abundance) - 0.5*Abundance)
  head( Taxonomies_x )
  
  # Taxonomies_x$label_y =
  Taxonomies_x = cbind(as.data.frame(zhnagxu3),as.data.frame(Taxonomies_x)[,-1])

  Taxonomies_x$label = Taxonomies_x$aa
  # #使用循环将堆叠柱状图柱子比较窄的别写标签,仅仅宽柱子写上标签
  # for(i in 1:nrow(Taxonomies_x)){
  #   if(Taxonomies_x[i,3] > 3){
  #     Taxonomies_x[i,5] = Taxonomies_x[i,5]
  #   }else{
  #     Taxonomies_x[i,5] = NA
  #   }
  # }

  Taxonomies_x$aa = factor(Taxonomies_x$aa,order = TRUE,levels = c(as.character(cc$aa)))
  
  ##普通柱状图
  
  p4 <- ggplot(Taxonomies_x , aes(x =  group, y = Abundance, fill = aa, order = aa)) +
    geom_bar(stat = "identity",width = 0.5,color = "black") +
    # scale_fill_manual(values = colors) +
    theme(axis.title.x = element_blank()) +
    theme(legend.text=element_text(size=6)) +
    scale_y_continuous(name = "Relative abundance (%)") +
    guides(fill = guide_legend(title = j)) +
    labs(x="",y="Relative abundance (%)",
         title= "")
  # paste("Top ",Top," ",j,sep = "")
  p4


    p4 = p4 + scale_x_discrete(limits = axis_order)

  
  
  if (sd == TRUE) {
    p4 =  p4 +
      geom_errorbar(aes(ymin=label_sd-sd, ymax=label_sd +sd), width=.2)
  }
  
  if (label == TRUE) {
    p4 = p4 +
      geom_text(aes(y = label_y, label = label ))
  }
  
  map = as.data.frame(phyloseq::sample_data(ps))
  if (length(unique(map$Group))>3){p4 = p4 + theme(axis.text.x=element_text(angle=45,vjust=1, hjust=1))}
  
  #-------------冲击图
  cs = Taxonomies_x $aa
  
  lengthfactor <- cs %>%
    levels() %>%
    length()
  cs4 <- cs %>%
    as.factor() %>%
    summary()  %>%
    as.data.frame()
  cs4$id = row.names(cs4)
  
  #对因子进行排序
  df_arrange<- dplyr::arrange(cs4, id)
  #对Taxonomies_x 对应的列进行排序
  Taxonomies_x1<- dplyr::arrange(Taxonomies_x , aa)
  head(Taxonomies_x1)
  #构建flow的映射列Taxonomies_x
  Taxonomies_x1$ID = factor(rep(c(1:lengthfactor), cs4$.))
  
  #colour = "black",size = 2,,aes(color = "black",size = 0.8)
  head(Taxonomies_x1)
  Taxonomies_x1$Abundance
#>  [1] 39.237397 40.722278 40.228699 15.599818 12.611327 16.144444 16.726732
#>  [8] 13.155390 14.172527  6.017195  9.123618  7.533416  6.089171  6.210184
#> [15]  6.063844  3.576966  4.740252  3.342673  3.661095  3.682421  4.121777
#> [22]  3.012819  3.265553  3.139758  3.001894  2.471411  2.291067  1.632678
#> [29]  2.740635  1.464264  1.444234  1.276930  1.497529
  
  p3 <- ggplot(Taxonomies_x1, aes(x = group, y = Abundance,fill = aa,alluvium = aa,stratum = ID)) +
    ggalluvial::geom_flow(aes(fill = aa, colour = aa),
              stat = "alluvium", lode.guidance = "rightleft",
              color = "black",size = 0.2,width = 0.35,alpha = .2)  +
    geom_bar(width = 0.45,stat = "identity") +
    labs(x="",y="Relative abundance (%)",
         title= "") +
    guides(fill = guide_legend(title = j),color = FALSE) +
    scale_y_continuous(expand = c(0,0))
  p3

  
  # flower plot
  p1 <- ggplot(Taxonomies_x1,
               aes(x = group, alluvium = aa, y = Abundance)) +
    ggalluvial::geom_flow(aes(fill = aa, colour = aa), width = 0)  +
    labs(x="",y="Relative abundance (%)",
         title="") +
    guides(fill = guide_legend(title = j),color = FALSE) +
    scale_y_continuous(expand = c(0,0))
  

    p1 = p1 + scale_x_discrete(limits = axis_order)
    p3 = p3 + scale_x_discrete(limits = axis_order)
    
  
  # p3
  if (label == TRUE) {
    p1 = p1 +
      geom_text(aes(y = label_y, label = label ))
    p3 = p3 +
      geom_text(aes(y = label_y, label = label ))
  }
  
  if (sd == TRUE) {
    p1 =  p1 +
      geom_errorbar(aes(ymin=label_sd-sd, ymax=label_sd +sd), width=.2)
    p3 =  p3 +
      geom_errorbar(aes(ymin=label_sd-sd, ymax=label_sd +sd), width=.2)
  }
  
  if (length(unique(map$Group))>3){ p3=p3+theme(axis.text.x=element_text(angle=45,vjust=1, hjust=1))}
  
  # return(list(p4,Taxonomies,p3,p1))


barpath = paste(otupath,"/Microbial_composition/",sep = "")
dir.create(barpath)

phyloseq::rank_names(ps)
#> [1] "Kingdom" "Phylum"  "Class"   "Order"   "Family"  "Genus"   "Species"



  p4_1 <- p4 + 
    # scale_fill_brewer(palette = "Paired") + 
    scale_fill_manual(values = colset3) +
    scale_x_discrete(limits = axis_order) +
    mytheme1
  p4_1


  p4_2  <- p3 + 
    # scale_fill_brewer(palette = "Paired") + 
    scale_fill_manual(values = colset3) +
    scale_x_discrete(limits = axis_order) + 
    mytheme1
  p4_2

  
  databar <- Taxonomies
  
  FileName1 <- paste(barpath,"/a2_",j,"_barflow",".pdf", sep = "")
  ggsave(FileName1, p4_2, width = (5+ gnum), height =8 )
  FileName2 <- paste(barpath,"/a2_",j,"_barflow",".jpg", sep = "")
  ggsave(FileName2, p4_2, width = (5+ gnum), height =8 )
  
  FileName1 <- paste(barpath,"/a2_",j,"_bar",".pdf", sep = "")
  ggsave(FileName1, p4_1, width = (5+ gnum), height =8 )
  FileName2 <- paste(barpath,"/a2_",j,"_bar",".jpg", sep = "")
  ggsave(FileName2, p4_1, width = (5+ gnum), height =8 )
  
  FileName <- paste(barpath,"/a2_",j,"_bar_data",".csv", sep = "")
  write.csv(databar,FileName)


detach("package:ggalluvial")

Species composition-circle strack bar diagram(物种组成-环状堆叠柱状图)

# BiocManager::install("ggstance")

ps = readRDS("./data/dataNEW/ps_16s.rds")

library(ggtree)

barpath = paste(otupath,"/circle_Micro_strack_bar/",sep = "");print(barpath)
#> [1] ".//result_and_plot//Base_diversity_16s/OTU//circle_Micro_strack_bar/"
dir.create(barpath)


Top = 15
dist = "bray"
cuttree = 3
hcluter_method = "complete"


  # phyloseq(ps)对象标准化
  otu = ps %>% 
    ggClusterNet::scale_micro() %>%
    ggClusterNet::vegan_otu() %>% t() %>%
    as.data.frame()
  # 导出OTU表
  # otu = as.data.frame(t(vegan_otu(ps1_rela)))
  
  #计算距离矩阵
  unif = phyloseq::distance(ps %>% ggClusterNet::scale_micro() , method = dist)
  # 聚类树,method默认为complete
  hc <- stats::hclust(unif, method = hcluter_method )
  #  take grouping with hcluster tree
  clus <- cutree(hc, cuttree )
  
  d = data.frame(label = names(clus), 
                 member = factor(clus))
  # eatract mapping file
  map = as.data.frame(phyloseq::sample_data(ps))
  
  dd = merge(d,map,by = "row.names",all = F)
  row.names(dd) = dd$Row.names 
  dd$Row.names = NULL
  # library(tidyverse)
  # ggtree绘图 #----
  p  = ggtree::ggtree(hc, layout='circular') %<+% dd + 
    geom_tippoint(size=5, shape=21, aes(fill= Group, x=x)) + 
    geom_tiplab(aes(color = Group,x=x * 1.2), hjust=1,offset=0.3) + xlim(-0.5,NA)
  p

  
  psdata =  ggClusterNet::tax_glom_wt(ps = ps %>% ggClusterNet::scale_micro(),ranks = "Phylum" )
  # 转化丰度值
  psdata = psdata%>% phyloseq::transform_sample_counts(function(x) {x/sum(x)} )
  
  #--提取otu和物种注释表格
  otu = phyloseq::otu_table(psdata)
  tax = phyloseq::tax_table(psdata)
  head(tax)
#> Taxonomy Table:     [6 taxa by 2 taxonomic ranks]:
#>                 Kingdom    Phylum           
#> Acidobacteria   "Bacteria" "Acidobacteria"  
#> Actinobacteria  "Bacteria" "Actinobacteria" 
#> Aminicenantes   "Bacteria" "Aminicenantes"  
#> Armatimonadetes "Bacteria" "Armatimonadetes"
#> Bacteroidetes   "Bacteria" "Bacteroidetes"  
#> BRC1            "Bacteria" "BRC1"
  #--按照指定的Top数量进行筛选与合并
  j = "Phylum"
  for (i in 1:dim(tax)[1]) {
    if (row.names(tax)[i] %in% names(sort(rowSums(otu), decreasing = TRUE)[1:Top])) {
      tax[i,j] =tax[i,j]
    } else {
      tax[i,j]= "Other"
    }
  }
  phyloseq::tax_table(psdata)= tax
  
  Taxonomies <- psdata %>% phyloseq::psmelt()
  
  Taxonomies$Abundance = Taxonomies$Abundance * 100
  
  Taxonomies$OTU = NULL
  colnames(Taxonomies)[1] = "id"
  
  head(Taxonomies)
  
  dat2 = data.frame(id = Taxonomies$id,Abundance = Taxonomies$Abundance,Phylum = Taxonomies$Phylum)
  head(dat2)
  
  p2 <- p + 
    ggnewscale::new_scale_fill() + 
    ggtreeExtra::geom_fruit(
      data=dat2,
      geom=geom_bar,
      mapping=aes(
        x=Abundance, 
        y=id, 
        fill= Phylum
      ),
      stat="identity",
      width = 0.4,
      orientation="y",
      offset=0.9,
      pwidth=2,
      axis.params=list(
        axis = "x", 
        text.angle = -45, 
        hjust = 0, 
        vjust = 0.5, 
        nbreak = 4
      )
    ) +
    scale_fill_manual(
      # name = "Number of interactions",
      values=c(colset3),
      guide=guide_legend(keywidth=0.5,keyheight=0.5,order=5)
    ) +theme_void()
  
  

FileName2 <- paste(barpath,"/a2_","_bar",".jpg", sep = "")
ggsave(FileName2, p2, width = 10, height =8 )

FileName2 <- paste(barpath,"/a2_","_bar",".pdf", sep = "")
ggsave(FileName2, p2, width = 10, height =8 )

#--距离和丰度合并#----

dist = "bray"
j = "Phylum"# 使用门水平绘制丰度图表
# rep = 6 # 重复数量是6个
Top = 10 # 提取丰度前十的物种注释
tran = TRUE # 转化为相对丰度值
hcluter_method = "complete"
Group = "Group"
cuttree = 3
  

  # phyloseq(ps)对象标准化
  ps1_rela = phyloseq::transform_sample_counts(ps, function(x) x / sum(x) )
  # 导出OTU表
  otu = as.data.frame(t(ggClusterNet::vegan_otu(ps1_rela)))
  
  #计算距离矩阵
  unif = phyloseq::distance(ps1_rela , method = dist)
  # 聚类树,method默认为complete
  hc <- stats::hclust(unif, method = hcluter_method )
  
  #  take grouping with hcluster tree
  clus <- stats::cutree(hc, cuttree )
  # 提取树中分组的标签和分组编号
  d = data.frame(label = names(clus), 
                 member = factor(clus))
  # eatract mapping file
  map = as.data.frame(phyloseq::sample_data(ps))
  # 合并树信息到样本元数据
  dd = merge(d,map,by = "row.names",all = F)
  row.names(dd) = dd$Row.names 
  dd$Row.names = NULL
  
  # library(tidyverse)
  # ggtree绘图 #----
  p  = ggtree::ggtree(hc) %<+% dd + 
    geom_tippoint(size=5, shape=21, aes(fill= Group, x=x)) + 
    geom_tiplab(aes(color = Group,x=x * 1.2), hjust=1)
  p

  # 按照分类学门(j)合并
  psdata =  ggClusterNet::tax_glom_wt(ps = ps1_rela,ranks = j)
  
  # 转化丰度值
  if (tran == TRUE) {
    psdata = psdata %>% phyloseq::transform_sample_counts(function(x) {x/sum(x)} )
  }
  
  #--提取otu和物种注释表格
  otu = phyloseq::otu_table(psdata)
  tax = phyloseq::tax_table(psdata)
  
  #--按照指定的Top数量进行筛选与合并
  for (i in 1:dim(tax)[1]) {
    if (row.names(tax)[i] %in% names(sort(rowSums(otu), decreasing = TRUE)[1:Top])) {
      tax[i,j] =tax[i,j]
    } else {
      tax[i,j]= "Other"
    }
  }
  phyloseq::tax_table(psdata)= tax
  
  ##转化为表格
  Taxonomies <- psdata %>% 
    phyloseq::psmelt()
  head(Taxonomies)
  Taxonomies$Abundance = Taxonomies$Abundance * 100
  
  Taxonomies$OTU = NULL
  colnames(Taxonomies)[1] = "id"
  
  head(Taxonomies)
  
  p <- p + ggnewscale::new_scale_fill()
  p

  p1 <- facet_plot(p, panel = 'Stacked Barplot', data = Taxonomies, geom = ggstance::geom_barh,mapping = aes(x = Abundance, fill = !!sym(j)),color = "black",stat='identity' )   
  p1

  
  grotax <- Taxonomies %>%
    dplyr::group_by(Group,!!sym(j)) %>%
    dplyr::summarise(Abundance = sum(Abundance))
  head(grotax)

  data = c()
  i = 2
  for (i in 1:length(unique(phyloseq::sample_data(psdata)$Group))) {
    a <- as.data.frame(table(phyloseq::sample_data(psdata)$Group))[i,1]
    
    b =  as.data.frame(table(phyloseq::sample_data(psdata)$Group))[i,2]
    
    c <- grotax %>% 
      dplyr::filter(Group == a)
    c$Abundance <- c$Abundance/b
    head(c)
    # data = data.frame(Sample =c$Sample,Abundance = c$Abundance,aa =c$aa,Group = c$Group)
    data = c
    if (i == 1) {
      table = data
    }
    if (i != 1) {
      table = rbind(table,data)
    }
    
  }
 sum( grotax$Abundance)
#> [1] 1800
  head(table)

#--绘制分组的聚类结果
  ps1_rela = phyloseq::transform_sample_counts(ps, function(x) x / sum(x) )
  #按照分组合并OTU表格
  
  otu = as.data.frame((ggClusterNet::vegan_otu(ps1_rela)))
  
  iris.split <- split(otu,as.factor(phyloseq::sample_data(ps1_rela)$Group))
  iris.apply <- lapply(iris.split,function(x)colMeans(x))
  # 组合结果
  iris.combine <- do.call(rbind,iris.apply)
  otuG = t(iris.combine)
  
  ps = phyloseq::phyloseq(phyloseq::otu_table(otuG,taxa_are_rows = T),
                          phyloseq::tax_table(ps1_rela)
                         
                          
  )
  
  hc = ps %>%
    phyloseq::distance(method = dist) %>%
    stats::hclust( method = hcluter_method )
  
  #  take grouping with hcluster tree
  clus <- cutree(hc, cuttree )
  # 提取树中分组的标签和分组编号
  d = data.frame(label = names(clus), 
                 member = factor(clus))
  # eatract mapping file
  
  map = data.frame(ID = unique(phyloseq::sample_data(ps1_rela)$Group),row.names = unique(phyloseq::sample_data(ps1_rela)$Group),Group = unique(phyloseq::sample_data(ps1_rela)$Group))
  # 合并树信息到样本元数据
  dd = merge(d,map,by = "row.names",all = F)
  row.names(dd) = dd$Row.names
  dd$Row.names = NULL
  
  # ggtree绘图 #----
  p3  = ggtree(hc) %<+% dd + 
    geom_tippoint(size=5, shape=21, aes(fill= member, x=x)) + 
    geom_tiplab(aes(color = member,x=x * 1.2), hjust=1)
  p3

  p3 <- p3 + ggnewscale::new_scale_fill()
  head(grotax)
  
  p4 <- facet_plot(p3, panel = 'Stacked Barplot', data = table, geom = ggstance::geom_barh,mapping = aes(x = Abundance, fill = !!sym(j)),color = "black",stat='identity' )   
  p4

  
# return(list(p,p1,p3,p4,Taxonomies))

  p5_1 <- p
  p5_2 <- p1
  p5_3 <- p3
  p5_4 <- p4
  clubardata <- Taxonomies
ps = readRDS("./data/dataNEW/ps_16s.rds")
  
  FileName1 <- paste(barpath,"/a2_",j,"_cluster_sample",".pdf", sep = "")
  ggsave(FileName1, p5_1, width = 6, height = dim(phyloseq::sample_data(ps))[1]/4,limitsize = FALSE)
  FileName1 <- paste(barpath,"/a2_",j,"_cluster_bar_sample",".pdf", sep = "")
  ggsave(FileName1, p5_2, width = 12, height = dim(phyloseq::sample_data(ps))[1]/4 ,limitsize = FALSE)
  FileName1 <- paste(barpath,"/a2_",j,"_cluster_sample",".jpg", sep = "")
  ggsave(FileName1, p5_1, width = 6, height =dim(phyloseq::sample_data(ps))[1]/4 ,limitsize = FALSE)
  FileName1 <- paste(barpath,"/a2_",j,"_cluster_bar_sample",".jpg", sep = "")
  ggsave(FileName1, p5_2, width = 12, height =dim(phyloseq::sample_data(ps))[1]/4 ,limitsize = FALSE)
  
  FileName1 <- paste(barpath,"/a2_",j,"_cluster_Group",".pdf", sep = "")
  ggsave(FileName1, p5_3, width = 6, height = gnum,limitsize = FALSE)
  FileName1 <- paste(barpath,"/a2_",j,"_cluster_bar_Group",".pdf", sep = "")
  ggsave(FileName1, p5_4, width = 12, height = gnum ,limitsize = FALSE)
  FileName1 <- paste(barpath,"/a2_",j,"_cluster_Group",".jpg", sep = "")
  ggsave(FileName1, p5_3, width = 6, height = gnum ,limitsize = FALSE)
  FileName1 <- paste(barpath,"/a2_",j,"_cluster_bar_Group",".jpg", sep = "")
  ggsave(FileName1, p5_4, width = 12, height = gnum ,limitsize = FALSE)
  
  FileName <- paste(barpath,"/a2_",j,"_cluster_bar_data",".csv", sep = "")
  write.csv(clubardata,FileName)

Ternary diagram(三元图)


library(ggtern)
ternpath = paste(otupath,"/ggtern/",sep = "")
dir.create(ternpath)
ps = readRDS("./data/dataNEW/ps_16s.rds")
ternpath = ternpath

  ps_rela = phyloseq::transform_sample_counts(ps, function(x) x / sum(x) );ps_rela 
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:         [ 37178 taxa and 18 samples ]
#> sample_data() Sample Data:       [ 18 samples by 2 sample variables ]
#> tax_table()   Taxonomy Table:    [ 37178 taxa by 7 taxonomic ranks ]
#> phy_tree()    Phylogenetic Tree: [ 37178 tips and 37176 internal nodes ]
#> refseq()      DNAStringSet:      [ 37178 reference sequences ]
  
  otu = ggClusterNet::vegan_otu(ps_rela) %>% as.data.frame()
  
  #数据分组
  iris.split <- split(otu,as.factor(as.factor(phyloseq::sample_data(ps)$Group)))
  #数据分组计算平均值
  iris.apply <- lapply(iris.split,function(x)colSums(x[]))
  # 组合结果
  iris.combine <- do.call(rbind,iris.apply)
  ven2 = t(iris.combine) %>% as.data.frame()
  
  head(ven2)
  A <- combn(colnames(ven2),3)
  ven2$mean = rowMeans(ven2)
  tax = ggClusterNet::vegan_tax(ps)
  otutax = cbind(ven2,tax)
  head(otutax)
  otutax$Phylum[otutax$Phylum == ""] = "Unknown"
  
  
  # i = 1

  for (i in 1:dim(A)[2]) {
    x = A[1,i]
    y = A[2,i]
    z = A[3,i]
    p <- ggtern::ggtern(data=otutax,aes_string(x = x,y=y,z=z,color = "Phylum",size ="mean" ))+geom_point() + theme_void()
    p
    filename = paste(ternpath,"/",paste(x,y,z,sep = "_"),"_OTU.pdf",sep = "")
    ggsave(filename,p,width = 12,height = 10)
    
    filename = paste(ternpath,"/",paste(x,y,z,sep = "_"),"_OTU.png",sep = "")
    ggsave(filename,p,width = 12,height = 10,dpi = 100)
    filename = paste(ternpath,"/",paste(x,y,z,sep = "_"),"_OTU.csv",sep = "")
    write.csv(otutax,filename,quote = FALSE)
  }

detach("package:ggtern")

Total microbe + Specific microbe (共有微生物+特有微生物)


flowpath = paste(otupath,"/flowplot/",sep = "")
dir.create(flowpath)


group="Group"
#rep=6
m1=2
start=1
a=0.2
b=1
lab.leaf=1
col.cir="yellow"
a.cir=0.5
b.cir=0.5
m1.cir=2
N=0.5

  
mapping = as.data.frame(phyloseq::sample_data(ps))
aa = ggClusterNet::vegan_otu(ps)
otu_table = as.data.frame(t(aa))
count = aa
sub_design <- as.data.frame(phyloseq::sample_data(ps))
sub_design$SampleType = sub_design$Group
phyloseq::sample_data(ps ) = sub_design
count[count > 0] <- 1
count2 = as.data.frame(count)
# group
iris.split <- split(count2,as.factor(sub_design$Group))
#group mean
iris.apply <- lapply(iris.split,function(x)colSums(x[]))
  # conbine result
  iris.combine <- do.call(rbind,iris.apply)
  ven2 = t(iris.combine)

  for (i in 1:length(unique(phyloseq::sample_data(ps)$Group))) {
    aa <- as.data.frame(table(phyloseq::sample_data(ps)$Group))[i,1]
    bb =  as.data.frame(table(phyloseq::sample_data(ps)$Group))[i,2]
    ven2[,aa] = ven2[,aa]/bb
  }

  ven2[ven2 < N]  = 0
  ven2[ven2 >=N]  = 1
  ven2 = as.data.frame(ven2)
  ven3 = as.list(ven2)
  ven2 = as.data.frame(ven2)
  all_num = dim(ven2[rowSums(ven2) == length(levels(sub_design$Group)),])[1]
  ven2[,1] == 1
#>     [1] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#>    [13]  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE
#>    [25]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#>    [37]  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
#>    [49]  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>    [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE
#>    [73]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#>    [85]  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#>    [97] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE
#>   [109] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [121] FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [133] FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#>   [145]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#>   [157]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
#>   [169]  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
#>   [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#>   [193] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>   [205] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE
#>   [217] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [229]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>   [241] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [253] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#>   [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [277] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#>   [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#>   [301] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#>   [313]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE
#>   [325] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE
#>   [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#>   [349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#>   [361] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#>   [373] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#>   [385] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#>   [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [409]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#>   [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>   [433] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>   [445] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE
#>   [457] FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE
#>   [469]  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#>   [481] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#>   [493] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE
#>   [505]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>   [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [529] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>   [541]  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>   [553] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
#>   [565]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>   [577]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#>   [589]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [601] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [613] FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [625] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#>   [637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [649] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [661]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>   [673] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>   [709] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>   [733]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#>   [745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>   [757] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE
#>   [769] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
#>   [781] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [793]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [817] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE
#>   [829] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [853]  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [877] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
#>   [889] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE
#>   [901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE
#>   [913] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#>   [925] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [937] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [949] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [961] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [973] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>   [985] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
#>   [997] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [1009]  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [1021] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1033] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1045] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [1057]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#>  [1069] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [1081] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1093] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1105] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [1117] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1129]  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
#>  [1141] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
#>  [1153]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#>  [1165] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#>  [1177]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE
#>  [1189] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [1201]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [1213] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE
#>  [1225] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [1237]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [1249]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#>  [1261] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#>  [1273] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1285] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [1297]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#>  [1309] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1321] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1333] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [1345] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [1357] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [1369] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1381] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#>  [1393]  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#>  [1405]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
#>  [1417] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE
#>  [1429] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#>  [1441] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [1453] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [1465]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#>  [1477] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1489] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [1501] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#>  [1513]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE
#>  [1525] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#>  [1537]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [1549]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1561] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1573] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [1585] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#>  [1597]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [1609] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
#>  [1621] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#>  [1633] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1645] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE
#>  [1657] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1669] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#>  [1681] FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [1693] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1705] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1717] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1729] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [1741]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE
#>  [1753] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [1765] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [1777]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1789] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [1801] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#>  [1813] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1825]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1837] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1849] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1861] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1873] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1885] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1897] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1909]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1921] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#>  [1933] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [1945]  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [1957] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1969]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1981]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [1993] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2005] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [2017]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2029]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#>  [2041] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2053] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2065] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#>  [2077] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [2089] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
#>  [2101] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2113] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2125] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2137] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2149] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2161] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2173] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2185] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2197]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2209] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2221] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2233] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [2245] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2257] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2269] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2281] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2293] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2305] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2317] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2329] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#>  [2341] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2353] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2365] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2377]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2389] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [2401] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2413] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [2425] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2437] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2449] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2461] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2473] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2485] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2497] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2509] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2521] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2533] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2545] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2557] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2569] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2581]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2593] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2605] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#>  [2617] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2629]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2641] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2653] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2665] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2677] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2689] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2701] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2713] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2725] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2737] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2749] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [2761] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
#>  [2773] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
#>  [2785] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2797] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE
#>  [2809] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#>  [2821] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2833] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [2845]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2857] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE
#>  [2869] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2881] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2893] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2905] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2917] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [2929] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [2941] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [2953] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#>  [2965] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#>  [2977] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [2989] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3001] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3013] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3025]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [3037] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [3049] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3061] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3073] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3085] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [3097] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE
#>  [3109] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
#>  [3121]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3133] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE
#>  [3145] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#>  [3157] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3181]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE
#>  [3193] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#>  [3205]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
#>  [3217]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
#>  [3229]  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#>  [3241] FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
#>  [3253]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#>  [3265]  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE
#>  [3277] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#>  [3289] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [3301]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE
#>  [3313] FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
#>  [3325]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3337]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#>  [3349] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [3361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3385] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3397]  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#>  [3409] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [3421] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#>  [3445] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [3457]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [3469] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [3481] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [3493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [3505] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [3529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#>  [3541] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE
#>  [3553]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#>  [3565]  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE
#>  [3577] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [3589]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#>  [3613]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3625] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#>  [3637]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [3649] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3661] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#>  [3673] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#>  [3685] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3697] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [3709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [3721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [3733] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE
#>  [3745] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [3757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#>  [3769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [3781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [3793]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE
#>  [3805]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3817] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3829] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [3841]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3865] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [3877]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [3889]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [3901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [3925]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
#>  [3937]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#>  [3949] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [3961]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE
#>  [3973] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
#>  [3985] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE
#>  [3997] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [4009] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [4021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4033]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4045] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4057]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#>  [4069] FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [4081] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
#>  [4093] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
#>  [4105] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [4117] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#>  [4129]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [4141]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4153] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [4165]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
#>  [4177]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [4189] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
#>  [4201]  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#>  [4213]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#>  [4225]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#>  [4237] FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE
#>  [4249] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4261]  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [4273]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [4285] FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#>  [4297] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4309] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4321] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4333]  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4345] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4357] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4369] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4381] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [4393] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4405] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4417] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#>  [4429] FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#>  [4441] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [4453]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4465] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4477] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4489] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#>  [4501] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#>  [4513]  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [4525] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE
#>  [4537] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#>  [4549] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#>  [4561] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4573] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4585] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#>  [4597]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#>  [4609] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [4621] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4633] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [4645] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4657] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4669] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4681] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [4693]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4705] FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#>  [4717]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [4729]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#>  [4741] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4753]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#>  [4765] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [4777] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [4789] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [4801] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#>  [4813] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4825]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [4837] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [4849] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [4861] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [4873] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
#>  [4885]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#>  [4897]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [4909] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#>  [4921]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [4933]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4945]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
#>  [4957] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [4969] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [4981]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE
#>  [4993]  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#>  [5005] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [5017] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5029] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [5041] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [5053] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#>  [5065] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE
#>  [5077] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [5089] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#>  [5101] FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#>  [5113] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [5125]  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5137] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5149] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5161] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
#>  [5173]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#>  [5185]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [5197] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5209] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5221] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [5233]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5245] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5257] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE
#>  [5269] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [5281]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5293] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5305] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5317] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5329] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5341] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#>  [5353]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5365] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [5377] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5389] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [5401] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [5413]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [5425] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [5437]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#>  [5449] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [5461] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#>  [5473] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5485] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5497] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5509] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5521] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5533] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5545] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [5557]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#>  [5569] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [5581] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [5593] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5605] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5617] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [5629] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [5641] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5653] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5665] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5677] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5689] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5701] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [5713] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5725] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5737] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#>  [5749] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5761] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5773] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5785] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [5797] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [5809] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5821] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5833] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5845] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5857] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [5869]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [5881] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5893] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5905] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5917] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5929] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [5941] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5953]  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#>  [5965] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [5977] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [5989] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6001] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#>  [6013] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6025] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6037] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6049]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6061] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [6073] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6085] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6097] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6109] FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#>  [6121] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [6169]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [6193] FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
#>  [6205] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [6217] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6229] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#>  [6241] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [6253]  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE
#>  [6265] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
#>  [6277]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#>  [6289] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [6301] FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [6325] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6337]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6361] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#>  [6373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6385]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#>  [6397] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [6409] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [6421] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [6433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#>  [6445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
#>  [6457]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [6481]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [6517]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#>  [6529] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6541] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
#>  [6553]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6565] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [6577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [6589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#>  [6601]  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#>  [6613]  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#>  [6625] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#>  [6637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#>  [6649] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#>  [6661] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
#>  [6673] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#>  [6685]  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE
#>  [6697] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#>  [6709]  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [6721]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [6745]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [6757] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6769] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6781]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#>  [6793]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
#>  [6805]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [6817] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6829] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [6841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6877]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [6889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6901] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [6913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#>  [6925] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6937] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [6949] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [6961] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6973] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [6985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [6997]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [7009] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7021] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#>  [7033] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#>  [7045] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7057] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [7069] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7081] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7093] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#>  [7105] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE
#>  [7117] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7129] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#>  [7141] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7153] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [7165] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7177]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7189] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE
#>  [7201] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [7213] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#>  [7225] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#>  [7237]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7249] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7261] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [7273] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7285] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [7297] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7309] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7321] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7333] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7345] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7357] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [7369] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7381] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [7393] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7405] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [7417] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7429] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7441]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [7453] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [7465] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [7477] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7489] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7501] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7513] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7525] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [7537] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [7549] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#>  [7561] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#>  [7573] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#>  [7585] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [7597] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7609] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7621] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7633] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7645] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [7657] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7669] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [7681] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7693] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7705]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7717] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7729] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7741] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7753] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7765] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7777] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7789] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7801] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7813] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7825] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7837] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7849] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7861] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7873] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
#>  [7885]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#>  [7897] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7909] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#>  [7921] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [7933] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7945] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE
#>  [7957] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7969] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [7981] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [7993] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [8005] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8017] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8029]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
#>  [8041] FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#>  [8053]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
#>  [8065] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8077] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [8089] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#>  [8101] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE
#>  [8113] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [8125]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
#>  [8137] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [8149] FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [8161] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8173] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#>  [8185] FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#>  [8197] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#>  [8209] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [8221] FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [8233] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [8245]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8257] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [8269] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#>  [8281] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE
#>  [8293] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [8305]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8317]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [8329] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [8341]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8353]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [8365] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8377] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8389]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#>  [8401] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8413] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8425] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [8437] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [8449] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#>  [8461] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [8473] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#>  [8485] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8497] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8509] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8521]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#>  [8533] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#>  [8545] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8557] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [8569] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8581] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#>  [8593] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8605] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8617] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8629] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8641] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8653] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8665]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8677] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8689] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8701] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8713] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [8725]  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8737] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8749] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8761] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [8773]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8785] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8797] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [8809]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [8821]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8833] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8845]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#>  [8857] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [8869] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#>  [8881]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE
#>  [8893] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8905] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#>  [8917]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8929] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [8941]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [8953] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [8965] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [8977] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [8989] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [9001] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [9013] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [9025] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#>  [9037] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9049] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9061] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9073] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE
#>  [9085] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [9097] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [9109] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [9121] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#>  [9133] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#>  [9145] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [9157]  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [9169] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#>  [9181] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [9193] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE
#>  [9205]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#>  [9217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [9229]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
#>  [9241]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#>  [9253] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#>  [9265] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9277] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9313] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [9325]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [9361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [9373]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [9385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9397] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9433] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [9481] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9493] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [9505] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9517]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [9529] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9541] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [9565] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [9577] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#>  [9589]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#>  [9613] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE
#>  [9625] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#>  [9637] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [9649]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#>  [9673]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#>  [9709] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
#>  [9721] FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
#>  [9733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#>  [9745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#>  [9757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9769] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#>  [9781] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#>  [9793]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9805] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE
#>  [9817] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#>  [9829]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
#>  [9841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9853]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#>  [9865] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9877] FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#>  [9889] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#>  [9901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9925] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#>  [9937] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9949] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#>  [9961] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#>  [9973] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
#>  [9985] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [9997]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10009]  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [10021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [10033] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10045] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [10057] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10069]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [10081] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [10093]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10105] FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [10117] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [10129] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
#> [10141]  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [10153] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [10165] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10177] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10189] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [10201] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [10213] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#> [10225] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10237]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [10249] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [10261] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10273] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10285]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [10297] FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [10309] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#> [10321]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [10333]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [10345] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [10357] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10369] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10381] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE
#> [10393] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [10405] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE
#> [10417] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10429]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [10441] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [10453] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10465] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE
#> [10477] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10489] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [10501] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE
#> [10513] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10525] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10537] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [10549]  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE
#> [10561] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
#> [10573]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [10585]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [10597] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE
#> [10609] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [10621] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [10633] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10645] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [10657] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10669]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10681] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [10693] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [10705] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10717] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [10729] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10741]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [10753]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [10765]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [10777] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10789] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [10801] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [10813] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10825] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [10837] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10849]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [10861]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10873] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [10885] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [10897]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10909] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10921] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10933] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10945] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [10957] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [10969]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE
#> [10981]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [10993] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [11005]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
#> [11017] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [11029] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [11041]  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [11053] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [11065] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [11077] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11089] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11101] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11113] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [11125] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [11137] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [11149] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [11161]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [11173] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [11185] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11197] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11209] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11221]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11233] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11245] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11257] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [11269] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11281] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE
#> [11293] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [11305] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [11317] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11329] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11341] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [11353] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11365] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11377] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11389] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11401] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [11413] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11425] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11437] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11449] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [11461] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [11473] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [11485] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11497] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [11509] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11521] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11533] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11545] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11557]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11569] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11581] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11593] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [11605] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [11617] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11629] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11641] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [11653] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11665] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11677] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11689] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [11701] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11713] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11725] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11737] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11749] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [11761] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11773] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [11785] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11797] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#> [11809] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11821] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11833] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [11845] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11857] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [11869] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [11881] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11893] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11905] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11917] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [11929] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11941] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [11953] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [11965] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11977] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [11989] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12001]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12013] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12025] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12037] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12049]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [12061] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [12073] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [12085] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [12097] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12121] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [12133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [12145] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [12157] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
#> [12169]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12181] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#> [12193] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [12205] FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [12217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [12229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [12241] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#> [12253] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [12265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [12289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [12301] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE
#> [12313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [12361] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [12373]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE
#> [12385] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
#> [12397] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12409] FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#> [12421]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12433] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [12445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [12457] FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [12469]  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
#> [12481] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [12493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12505] FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12517] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [12529] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [12541] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
#> [12553] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#> [12565] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [12577]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [12589]  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE
#> [12601] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE
#> [12613]  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
#> [12625] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
#> [12637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [12649] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [12661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12673] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
#> [12685] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#> [12709]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12721]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
#> [12733]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [12745] FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#> [12757]  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
#> [12769] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [12781]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#> [12793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [12817] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [12829] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12853] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12865] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [12877] FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE
#> [12889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [12901] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#> [12913]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12925] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [12937] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
#> [12949]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#> [12961] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12973] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [12985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [12997] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [13009] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [13021]  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
#> [13033] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [13045] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
#> [13057]  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [13069] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13081] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [13093] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [13105] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE
#> [13117] FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
#> [13129] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [13141] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13153] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [13165]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
#> [13177]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13189] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13201] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [13213]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [13225]  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13237] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE
#> [13249]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE
#> [13261] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13273] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [13285] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE
#> [13297] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
#> [13309] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#> [13321]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#> [13333]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#> [13345] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [13357] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13369] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13381] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [13393] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [13405]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [13417] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [13429] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
#> [13441] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [13453]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [13465] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [13477]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [13489] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13501] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [13513] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [13525]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [13537]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
#> [13549] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [13561]  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
#> [13573]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [13585] FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [13597] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [13609]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [13621]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [13633]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE
#> [13645] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
#> [13657]  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13669]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13681] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [13693] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13705] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13717] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [13729] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [13741]  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [13753] FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [13765] FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [13777] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [13789]  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
#> [13801]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13813] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
#> [13825] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13837]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
#> [13849]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [13861]  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#> [13873] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13885] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [13897] FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [13909]  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE
#> [13921] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [13933] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [13945] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
#> [13957]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13969] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
#> [13981]  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
#> [13993]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [14005] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [14017]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [14029] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [14041]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [14053] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [14065] FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
#> [14077]  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE
#> [14089] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [14101] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [14113] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [14125] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [14137]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [14149] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [14161] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [14173] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [14185] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [14197] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [14209] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [14221] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
#> [14233] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [14245] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [14257]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#> [14269] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [14281] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [14293]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [14305]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [14317] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [14329] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
#> [14341]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [14353] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [14365] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [14377] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [14389] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [14401] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [14413] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [14425] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [14437]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [14449] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [14461] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [14473]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [14485] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [14497]  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [14509] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [14521] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
#> [14533]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [14545] FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [14557] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [14569] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
#> [14581] FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
#> [14593]  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
#> [14605]  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
#> [14617] FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [14629] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#> [14641]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#> [14653]  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [14665] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#> [14677]  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
#> [14689] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [14701] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [14713]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [14725] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [14737] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
#> [14749]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [14761]  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#> [14773] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#> [14785]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [14797] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [14809]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [14821] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [14833] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#> [14845] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [14857] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#> [14869]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE
#> [14881]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
#> [14893]  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [14905] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [14917] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [14929] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#> [14941]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [14953]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#> [14965] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#> [14977] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [14989] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE
#> [15001] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15013] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [15025] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15037] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
#> [15049] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [15061]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15073] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15085] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [15097] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [15109] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
#> [15121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [15145] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15157] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15181] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [15193] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE
#> [15205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [15241] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15253] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [15277]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [15289] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15313] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [15337] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [15349] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [15361] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
#> [15373]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [15397] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE
#> [15409] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
#> [15421] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE
#> [15433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15445]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [15457] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [15469]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [15481]  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [15493] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15505]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE
#> [15517] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [15529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [15541]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [15553] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE
#> [15565] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [15577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE
#> [15589] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15601] FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [15613] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15625] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [15637] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [15649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [15661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [15673] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE
#> [15685]  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#> [15697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [15709] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [15721]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [15733] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [15745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15757] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [15769] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [15781]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [15793] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#> [15805] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [15817] FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [15829]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [15841]  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [15853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15877] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [15901] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE
#> [15913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15925] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15937] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [15949] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [15961] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [15973] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [15985] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE
#> [15997] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16009] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16021] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16033] FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#> [16045] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16057]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16069] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16081] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE
#> [16093] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16105] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [16117] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [16129] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16141] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [16153] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16165] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [16177] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [16189] FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [16201] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [16213] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [16225] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16237] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [16249] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [16261] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [16273] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16285] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [16297] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16309] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [16321]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16333]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [16345] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [16357]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [16369] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [16381] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16393]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16405] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [16417] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE
#> [16429] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16441] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16453] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16465] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [16477] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16489] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16501] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16513] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE
#> [16525] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [16537] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16549] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16561] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16573]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [16585] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16597] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16609]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [16621]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16633] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16645]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [16657] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16669] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16681] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16693] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16705] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16717] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16729] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16741] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16753] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16765] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16777] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16789] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16801] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16813] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16825] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16837] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16849] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16861] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [16873] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16885] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16897] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [16909] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16921] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16933] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [16945]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [16957] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [16969] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [16981] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [16993] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [17005] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [17017] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [17029] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17041] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17053] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [17065] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17077]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17089] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
#> [17101]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17113] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17125]  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [17137]  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [17149]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17161] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [17173] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [17185] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17197] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [17209]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17221] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [17233] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [17245] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [17257]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [17269] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17281] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [17293]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [17305] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17317] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17329] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [17341] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [17353]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [17365] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [17377]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17389] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [17401] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [17413] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [17425] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [17437] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [17449] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [17461]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17473] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [17485] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17497] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17509]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17521] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [17533] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [17545] FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [17557] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [17569] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
#> [17581]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17593] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [17605] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [17617] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [17629] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17641] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17653] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [17665] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [17677]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [17689] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [17701] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17713] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [17725] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17737] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [17749] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [17761] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [17773] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17785] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17797] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17809] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17821] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [17833] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17845] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17857] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [17869] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [17881] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17893] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17905] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17917] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [17929] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [17941] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17953] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [17965]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [17977] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [17989] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18001] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [18013] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [18025] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#> [18037] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18049] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [18061] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18073] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [18085]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [18097] FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [18109] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [18121] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18133] FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#> [18145] FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [18157] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [18169] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE
#> [18181] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [18193] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [18205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18217] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
#> [18229]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [18241] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [18253] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [18265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [18277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18289] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [18301] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [18313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [18325] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE
#> [18337]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18349] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [18373]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18385]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [18397]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [18409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18421] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [18433] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [18445] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [18457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [18469] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [18481] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [18505]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE
#> [18517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [18553] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [18565] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [18601] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18613]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [18637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [18649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [18733] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18745] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE
#> [18757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [18769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [18781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18793] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [18805]  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [18817] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [18829] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [18841] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [18853] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
#> [18865]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [18877] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#> [18889] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [18901] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#> [18913]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [18925] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#> [18937] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
#> [18949]  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [18961] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [18973] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [18985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [18997] FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [19009] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [19021] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [19033] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [19045] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [19057] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
#> [19069] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [19081] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [19093] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [19105] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [19117] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [19129] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [19141] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [19153] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [19165] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [19177] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [19189] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [19201] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [19213] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [19225] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [19237] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [19249] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [19261]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [19273] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [19285] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [19297] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [19309] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [19321] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [19333]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
#> [19345] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [19357] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [19369] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [19381] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [19393]  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [19405] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [19417] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [19429]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE
#> [19441]  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [19453] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [19465] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [19477] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [19489]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [19501] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#> [19513] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE
#> [19525] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [19537]  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
#> [19549] FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE
#> [19561] FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
#> [19573]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE
#> [19585] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#> [19597]  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [19609] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [19621] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#> [19633] FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE
#> [19645] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
#> [19657] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [19669]  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [19681]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
#> [19693]  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [19705] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE
#> [19717] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [19729]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [19741] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [19753] FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [19765]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE
#> [19777] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [19789] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [19801] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [19813] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [19825] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [19837]  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE
#> [19849]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
#> [19861] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [19873] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [19885]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#> [19897] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [19909] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [19921]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
#> [19933]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [19945]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [19957] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [19969] FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#> [19981] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [19993] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20005] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20017] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [20029] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#> [20041] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20053] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [20065]  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20077] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20089] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [20101] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE
#> [20113] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20125] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20137] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [20149] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20161] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20173] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20185] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [20197] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [20209] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE
#> [20221]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#> [20233] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [20245] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20257] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [20269] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [20281] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [20293] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20305] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [20317] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [20329]  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [20341] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [20353] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [20365]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [20377]  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [20389] FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE
#> [20401] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [20413] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [20425] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [20437] FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE
#> [20449] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20461] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20473] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20485] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [20497]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [20509] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20521] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#> [20533] FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [20545] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [20557] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
#> [20569] FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
#> [20581]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [20593] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20605] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20617] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [20629]  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [20641] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20653] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#> [20665]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [20677]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20689] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [20701] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [20713]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20725] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE
#> [20737] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [20749] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [20761]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [20773] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [20785]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [20797] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [20809] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [20821] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE
#> [20833]  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE
#> [20845] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
#> [20857] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [20869] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [20881] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [20893] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [20905]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20917] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [20929] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [20941] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20953] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [20965] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [20977]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [20989]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [21001] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [21013] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [21025] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [21037] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [21049] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21061] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21073] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE
#> [21085] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
#> [21097] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21109] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [21121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21145]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21157] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [21169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [21205] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [21241] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21253]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [21265] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [21277]  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21289] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21313] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [21325] FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [21337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [21349]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21361] FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [21373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [21397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21409] FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21433]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [21445] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [21457] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21481] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE
#> [21493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [21505] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21529] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [21541] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [21553] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [21601]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21625]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21637] FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [21649]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
#> [21673] FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21685] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21697]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21709] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21721] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [21733] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [21745]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [21757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [21769]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [21793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [21805] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [21817] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [21829]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21841] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21853]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [21865] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#> [21877] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [21889] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
#> [21901] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [21913]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [21925] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21937] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21949] FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [21961] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE
#> [21973] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [21985] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE
#> [21997] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [22009] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#> [22021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [22033] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
#> [22045] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22057] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [22069] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22081] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22093] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22105] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [22117] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22129] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [22141] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [22153] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [22165] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [22177] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [22189] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [22201] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [22213] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [22225] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22237] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [22249] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [22261]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [22273] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [22285] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [22297] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22309] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22321] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22333] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [22345] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [22357] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [22369] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [22381]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22393] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [22405] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22417] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22429]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22441] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22453]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22465] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [22477] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22489] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22501] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [22513] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22525] FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [22537] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [22549] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [22561]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE
#> [22573] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [22585] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [22597] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE
#> [22609] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [22621] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22633]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22645] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [22657]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22669]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE
#> [22681] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [22693]  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [22705] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [22717] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#> [22729] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [22741]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#> [22753] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
#> [22765]  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [22777] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [22789]  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [22801] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [22813] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [22825] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [22837] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22849] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [22861] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [22873]  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22885]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22897]  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22909] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
#> [22921] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE
#> [22933] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE
#> [22945] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
#> [22957]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
#> [22969]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [22981] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [22993] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE
#> [23005] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [23017] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23029] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
#> [23041] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [23053] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [23065]  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE
#> [23077]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#> [23089]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [23101] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [23113] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23125] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [23137] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [23149] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [23161]  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23173] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [23185]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#> [23197]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE
#> [23209] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#> [23221] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE
#> [23233] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [23245]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [23257] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [23269]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE
#> [23281] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [23293] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
#> [23305] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
#> [23317]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [23329] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [23341]  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [23353] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#> [23365] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE
#> [23377] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23389] FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [23401]  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23413] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23425] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [23437] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23449]  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [23461] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [23473]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [23485] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23497]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23509] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [23521] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23533] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23545] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [23557] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23569]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23581] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23593] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23605] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23617] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23629] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23641] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23653] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23665] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23677] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23689] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23701] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23713] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23725] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23737] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [23749] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23761] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [23773] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23785] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23797] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [23809] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23821] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [23833]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23845]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [23857] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [23869] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23881] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23893] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23905] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23917] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [23929] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [23941] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23953] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23965] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23977] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23989] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [24001] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [24013]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [24025] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [24037] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [24049]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [24061] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [24073]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [24085] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [24097] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [24109] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [24121]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [24133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [24145] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE
#> [24157] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [24169]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [24181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [24193]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
#> [24205] FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [24217]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
#> [24229]  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [24241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [24253] FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE
#> [24265] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [24277] FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
#> [24289] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [24301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [24313]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [24325] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [24337] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#> [24349] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [24361] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [24373] FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
#> [24385]  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [24397] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [24409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [24421] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE
#> [24433]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
#> [24445]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [24457] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
#> [24469] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [24481]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [24493]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [24505]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
#> [24517]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [24529] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [24541] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [24553] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [24565] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [24577]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [24589] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [24601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [24613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [24625]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#> [24637] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
#> [24649] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE
#> [24661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [24673] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [24685] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [24697]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [24709] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [24721] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [24733] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [24745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
#> [24757]  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [24769] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [24781]  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [24793] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [24805] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE
#> [24817]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#> [24829] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#> [24841]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
#> [24853]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [24865] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [24877] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [24889] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [24901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [24913] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [24925] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [24937] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [24949] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [24961] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [24973]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [24985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [24997] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25009] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [25021] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE
#> [25033]  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
#> [25045]  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE
#> [25057]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE
#> [25069]  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE
#> [25081]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#> [25093] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE
#> [25105]  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#> [25117] FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
#> [25129]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [25141] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE
#> [25153]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
#> [25165]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [25177]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
#> [25189]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#> [25201] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [25213] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [25225] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#> [25237] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
#> [25249] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [25261] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#> [25273] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25285]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE
#> [25297] FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#> [25309] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [25321]  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE
#> [25333]  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [25345] FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [25357] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [25369]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25381] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE
#> [25393]  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
#> [25405]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [25417] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25429] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE
#> [25441]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [25453] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [25465] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [25477]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#> [25489] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE
#> [25501]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
#> [25513] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [25525] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [25537] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
#> [25549] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [25561] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#> [25573] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#> [25585] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE
#> [25597] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [25609] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE
#> [25621]  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
#> [25633]  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25645] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [25657] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
#> [25669] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE
#> [25681] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25693] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25705] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [25717]  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
#> [25729]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25741] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25753] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25765] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25777] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [25789] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25801] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25813] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [25825] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [25837] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25849] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25861] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25873] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25885] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [25897]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25909] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [25921] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [25933] FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25945] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25957] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25969] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [25981] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [25993] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26005] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26017] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [26029] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [26041]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26053]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26065] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26077] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [26089] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [26101]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [26113] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26125] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [26137]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26149] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26161] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26173] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26185] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26197] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [26209] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26221] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26233] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [26245] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [26257]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [26269] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [26281] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26293] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
#> [26305]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE
#> [26317] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [26329] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [26341] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [26353] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [26365]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26377] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [26389]  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#> [26401] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE
#> [26413]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [26425] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26437]  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [26449] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26461] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [26473] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [26485] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [26497]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26509]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [26521] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [26533] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26545] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26557] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [26569] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [26581] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
#> [26593] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [26605] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [26617] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE
#> [26629]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#> [26641]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
#> [26653]  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#> [26665]  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#> [26677]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
#> [26689]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#> [26701]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#> [26713] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26725] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [26737] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#> [26749] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
#> [26761]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [26773] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [26785] FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [26797] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
#> [26809] FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [26821] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [26833] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [26845] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [26857] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26869] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [26881] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [26893] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE
#> [26905] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE
#> [26917] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26929] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
#> [26941] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26953] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [26965]  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [26977] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [26989] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27001] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [27013] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27025]  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
#> [27037]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#> [27049] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [27061] FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [27073]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [27085] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27097] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27109] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [27121] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [27133] FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE
#> [27145] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27157] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27193] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27205] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [27217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [27229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27241] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [27253] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [27265] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [27289] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [27301]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [27325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27337]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27361] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27373] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [27385]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27397]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27409] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27421] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [27433] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [27445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27457] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [27481] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27493] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [27505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27517] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [27529] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27553] FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE
#> [27565]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [27577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE
#> [27589] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [27601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [27613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27625]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [27637]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [27649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [27673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [27685] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [27697] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27709] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [27721]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27733] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [27745] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27757] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27769] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [27781] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [27793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [27805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
#> [27817] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [27829] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
#> [27841]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE
#> [27853]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#> [27865]  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE
#> [27877]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [27889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#> [27901] FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [27913] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE
#> [27925]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#> [27937]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27949] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27961] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27973] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [27985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [27997] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28009] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [28033] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
#> [28045]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28057] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [28069] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28081] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28093] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [28105] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28117] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28129] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [28141] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28153] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [28165]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE
#> [28177]  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [28189]  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [28201] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28213] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [28225] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [28237] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [28249] FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28261] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28273] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [28285] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [28297] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [28309] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28321] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE
#> [28333]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28345] FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28357] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [28369] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [28381] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28393] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28405] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [28417]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28429] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28441]  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28453] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [28465] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28477] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [28489]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28501] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28513] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#> [28525] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28537] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [28549] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#> [28561]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [28573] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28585] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28597]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [28609] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [28621] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28633] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [28645] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [28657] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [28669] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [28681] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28693] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [28705]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [28717]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28729] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [28741] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28753] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28765] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28777] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE
#> [28789] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [28801]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28813] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28825] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28837] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28849] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28861] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28873] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28885] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE
#> [28897] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE
#> [28909] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [28921] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#> [28933] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [28945] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [28957] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [28969] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [28981] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [28993]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29005] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [29017] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
#> [29029] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29041] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29053] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [29065] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE
#> [29077] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29089]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE
#> [29101] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
#> [29113] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29125] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [29137] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29149] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29161] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [29173] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29185] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [29197] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29209] FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [29221] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29233] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [29245] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29257] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29269] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29281] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [29293] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [29305] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29317] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29329] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29341] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [29353] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29365] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [29377] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29389] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29401] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29413]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29425] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [29437] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29449] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [29461] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [29473] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [29485] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29497] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [29509] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29521] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [29533]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [29545] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29557] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [29569] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29581] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [29593] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29605] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [29617] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29629] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29641] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29653] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [29665] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE
#> [29677] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29689] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29701] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29713] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [29725] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29737] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29749] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29761] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [29773] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#> [29785] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [29797] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [29809] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [29821] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [29833] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE
#> [29845] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29857] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [29869] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29881] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29893] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29905] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29917] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [29929] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [29941] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [29953] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [29965] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [29977] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE
#> [29989] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30001] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [30013] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [30025] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [30037] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30049] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
#> [30061] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30073] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30085] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [30097] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30109] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30121] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [30133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30157]  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [30169] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30181]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30193]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [30205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [30217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30253] FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [30265] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30289]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [30301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [30313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [30325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [30337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [30361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30385] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30397]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [30409] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [30445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [30457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30469] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [30481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30493]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30505] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
#> [30517] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [30529] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [30541]  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
#> [30553]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
#> [30565] FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [30577] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [30589]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [30601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [30613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30625] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [30637] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [30649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [30661] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30673] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE
#> [30685]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [30697]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [30709] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30733] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30745]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [30757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [30769] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [30781]  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [30793] FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [30805] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#> [30817] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
#> [30829]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#> [30841] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE
#> [30853]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [30865]  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
#> [30877] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE
#> [30889] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
#> [30901]  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [30913] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [30925] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE
#> [30937]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
#> [30949] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [30961] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
#> [30973] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [30985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [30997] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [31009] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [31021]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31033] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [31045] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [31057] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [31069]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [31081]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31093] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31105] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
#> [31117] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#> [31129] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [31141]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [31153] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [31165]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [31177] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [31189] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [31201] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [31213]  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [31225]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [31237] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [31249] FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [31261] FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#> [31273]  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
#> [31285] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [31297] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#> [31309] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE
#> [31321] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
#> [31333]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [31345] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [31357] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [31369] FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
#> [31381]  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE
#> [31393] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#> [31405] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31417]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
#> [31429]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [31441]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
#> [31453]  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#> [31465] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
#> [31477]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#> [31489] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE
#> [31501] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [31513] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [31525] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
#> [31537]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31549] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31561]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [31573] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [31585]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#> [31597] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE
#> [31609] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [31621] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [31633] FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [31645] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [31657] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [31669] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31681] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31693] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE
#> [31705] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31717] FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [31729] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [31741] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#> [31753] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31765] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [31777] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31789] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [31801] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [31813]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [31825] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31837] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [31849] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [31861] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [31873]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31885] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31897] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [31909] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31921] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [31933] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [31945] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31957] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31969] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [31981] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31993] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32005] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [32017] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32029] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32041] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [32053] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32065] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32077] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#> [32089] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32101] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [32113] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32125] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [32137] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [32149] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [32161]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32173] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32185] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32197] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [32209] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32221]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32233] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [32245] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32257] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32269] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32281] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [32293] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32305] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32317] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [32329] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [32341] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [32353] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [32365] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [32377] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32389]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [32401] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32413] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32425] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32437] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32449] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32461] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32473] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [32485] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32497] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [32509] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32521]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [32533] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32545] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [32557]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [32569] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32581] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32593] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [32605] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [32617] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [32629] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [32641] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32653] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32665] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32677] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32689] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [32701] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [32713] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32725]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32737] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32749]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [32761] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [32773] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32785] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32797] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32809] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [32821] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32833] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [32845] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32857] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [32869] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32881] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32893] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32905] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32917] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32929] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32941]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [32953] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32965] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32977] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [32989] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33001] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [33013] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33025] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33037] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33049] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33061] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [33073] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33085] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [33097] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [33109]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33121]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33133]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33157] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE
#> [33169]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [33181] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [33193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33205] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [33217] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [33229] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE
#> [33241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33277]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33289]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33313] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33337] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [33349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#> [33361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33373] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33385] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [33397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33409]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [33421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33433] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [33445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33469] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [33517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33529] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33565] FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [33577]  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33589] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [33601] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
#> [33613] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE
#> [33625] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
#> [33637]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE
#> [33649]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [33661] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [33673] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [33685]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33697]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [33721] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33733]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [33745]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33793] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [33805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33817] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [33829]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [33841] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [33853] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE
#> [33865] FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [33877] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [33889] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#> [33901] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
#> [33913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33925] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [33937] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [33949] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [33961] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [33973] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [33985]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [33997] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34009] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34021] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [34033]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [34045]  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [34057] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [34069] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [34081]  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
#> [34093] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [34105] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [34117] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [34129]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#> [34141] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34153] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [34165] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [34177] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [34189] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
#> [34201]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34213] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34225] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34237] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [34249]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34261] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34273] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [34285] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [34297] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [34309] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [34321]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [34333] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [34345] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [34357]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34369] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [34381] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34393] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34405] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34417] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34429] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34441] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [34453] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [34465] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34477] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34489] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [34501] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34513] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34525] FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [34537] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE
#> [34549]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [34561] FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [34573] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [34585]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [34597] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE
#> [34609]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [34621] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [34633] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [34645] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [34657] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34669] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [34681]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [34693] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [34705] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [34717] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [34729] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34741] FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [34753] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [34765] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [34777] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34789] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [34801]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [34813]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [34825]  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
#> [34837]  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE
#> [34849] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34861] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [34873] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [34885] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34897] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34909]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34921] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [34933] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34945] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [34957] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34969] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34981] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [34993] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35005] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35017] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35029] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [35041] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [35053] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [35065] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35077] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [35089] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [35101]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35113] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE
#> [35125] FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#> [35137] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35149] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [35161] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [35173] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [35185] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [35197] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35209] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [35221] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35233] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [35245]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35257]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [35269] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35281] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [35293] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [35305] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35317] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35329] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#> [35341]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35353] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35365] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35377] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35389] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [35401] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [35413] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [35425] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [35437] FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
#> [35449] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [35461] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [35473] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE
#> [35485] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35497] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [35509] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
#> [35521] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [35533]  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [35545] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [35557] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [35569] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [35581] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [35593] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35605]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#> [35617] FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
#> [35629]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [35641] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#> [35653] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [35665] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35677]  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [35689] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [35701]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> [35713]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35725] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35737] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [35749] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [35761] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [35773] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35785] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [35797]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [35809] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35821]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [35833] FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35845] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
#> [35857] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35869] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [35881] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE
#> [35893] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [35905] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [35917]  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [35929] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35941] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35953] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [35965] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [35977] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [35989] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36001] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36013] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36025] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [36037] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [36049] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [36061] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36073] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36085] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36097] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [36109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36121]  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [36133] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#> [36145]  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36157] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [36169]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36181] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [36193]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [36205]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [36217]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [36277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [36289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [36301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36313] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36325] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#> [36337] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [36349] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [36361] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [36373] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36397]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE
#> [36409]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [36421] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [36433] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [36445]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [36457]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [36469] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [36481] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [36517]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [36529] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [36541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [36553]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36577] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [36589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [36601] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE
#> [36613] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36625] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE
#> [36637] FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
#> [36649]  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE
#> [36661]  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#> [36673]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [36685] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#> [36709] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
#> [36721]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
#> [36733] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE
#> [36745] FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
#> [36757]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
#> [36769] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [36781]  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [36793] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#> [36805]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
#> [36817] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [36829]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#> [36841] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> [36853]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [36865] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE
#> [36877]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
#> [36889] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [36901]  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [36913] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [36925]  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [36937]  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [36949] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36961] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [36973] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [36985] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE
#> [36997]  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE
#> [37009] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [37021] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [37033] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE
#> [37045]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#> [37057] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
#> [37069] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [37081] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [37093] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
#> [37105]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE
#> [37117] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
#> [37129]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
#> [37141] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37153] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [37165] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37177] FALSE FALSE
  A = rep("A",length(colnames(ven2)))
  B = rep(1,length(colnames(ven2)))
  i = 1
  for (i in 1:length(colnames(ven2))) {
    B[i] = length(ven2[rowSums(ven2) == 1,][,i][ven2[rowSums(ven2) == 1,][,i] == 1])
    A[i] = colnames(ven2)[i]
  }
  n   <- length(A)
  deg <- 360 / n
  t = 1:n
  print(deg)
#> [1] 120
  
  p <- ggplot() +
    # geom_point(aes(x = 5 + cos((start + deg * (t - 1)) * pi / 180) * lab.leaf, y = 5 + sin((start + deg * (t - 1)) * pi / 180) *lab.leaf)) +
    ggforce::geom_ellipse(aes(x0 = 5 + cos((start + deg * (t - 1)) * pi / 180),
                     y0 = 5 + sin((start + deg * (t - 1)) * pi / 180),
                     a = a,
                     b = b,
                     angle = (n/2 +seq(0,1000,2)[1:n])/n * pi,
                     m1 = m1,
                     fill = as.factor(1:n)),show.legend = F) +
    ggforce::geom_ellipse(aes(x0 = 5,y0 = 5,a = a.cir,b = b.cir,angle = 0,m1 = m1.cir),fill = col.cir) +
    geom_text(aes(x = 5,y = 5,label = paste("OVER :",all_num,sep = ""))) +
    geom_text(aes(
      x = 5 + cos((start + deg * (t - 1)) * pi / 180) * lab.leaf,
      y = 5 + sin((start + deg * (t - 1)) * pi / 180) * lab.leaf,
      label = paste(A,":",B,sep = "")),angle = 360/n*((1:n)-1)  ) +
    coord_fixed() + theme_void()




p0_2 = p

FileName1 <- paste(flowpath,"ggflowerGroup.pdf", sep = "")
ggsave(FileName1, p0_2, width = 14, height = 14)
FileName2 <- paste(flowpath,"ggflowerGroup.jpg", sep = "")
ggsave(FileName2, p0_2, width = 14, height = 14 )

Venn diagram(维恩图)

  Venpath = paste(otupath,"/Ven_Upset_super/",sep = "")
  dir.create(Venpath)

group = "Group"
path = path
N = 0.5

  
  aa =  ggClusterNet::vegan_otu(ps)
  otu_table = as.data.frame(t(aa))
  count = aa
  countA = count
  
  sub_design <- as.data.frame(phyloseq::sample_data(ps))
  

  #
  # pick_val_num <- rep/2
  count[count > 0] <- 1
  count2 = as.data.frame(count )
  aa = sub_design[,"Group"]
  colnames(aa) = "Vengroup"
  
  #-div group
  iris.split <- split(count2,as.factor(aa$Vengroup))
  #数据分组计算平均值
  iris.apply <- lapply(iris.split,function(x)colSums(x[]))
  # 组合结果
  iris.combine <- do.call(rbind,iris.apply)
  ven2 = t(iris.combine)
  for (i in 1:length(unique(phyloseq::sample_data(ps)[,"Group"]))) {
    aa <- as.data.frame(table(phyloseq::sample_data(ps)[,"Group"]))[i,1]
    bb =  as.data.frame(table(phyloseq::sample_data(ps)[,"Group"]))[i,2]
    ven2[,aa] = ven2[,aa]/bb
  }
  ven2[ven2 < N]  = 0
  ven2[ven2 >=N]  = 1
  ven2 = as.data.frame(ven2)
  #
  ven3 = as.list(ven2)
  
  # ven_pick = get.venn.partitions(ven3)
  
  for (i in 1:ncol(ven2)) {
    
    
    ven3[[i]] <-  row.names(ven2[ven2[i] == 1,])
    
  }
  
  
  if (length(names(ven3)) == 2) {
    filename3 = paste(path,"ven_",paste(names(ven3),sep = "",collapse="-"),".pdf",sep = "",collapse="_")
    pdf(file=filename3,width = 12, height = 8)
    a<- VennDiagram::venn.diagram(ven3,
                    filename=NULL,
                    lwd=2,#圈线粗度
                    lty=1, #圈线类型
                    fill=c('red',"blue"), #填充颜色
                    col=c('red',"blue"), #圈线颜色
                    cat.col=c('red',"blue"),#A和B的颜色
                    cat.cex = 4,# A和B的大小
                    rotation.degree = 0,#旋转角度
                    main = "",#主标题内容
                    main.cex = 2,#主标题大小
                    sub = "",#亚标题内容
                    sub.cex = 1,#亚标题字大小
                    cex=3,#里面交集字的大小
                    alpha = 0.5,#透明度
                    reverse=TRUE,
                    scaled     = FALSE)
    grid.draw(a)
    dev.off()
    filename33 = paste(path,"ven",".jpg",sep = "",collapse="_")
    jpeg(file=filename33)
    grid.draw(a)
    dev.off();
    
    
    
    
    
  } else if (length(names(ven3)) == 3) {
    filename3 = paste(path,"ven_",paste(names(ven3),sep = "",collapse="-"),".pdf",sep = "",collapse="_")
    pdf(file=filename3,width = 18, height = 15)
    a<- VennDiagram::venn.diagram(ven3,
                    filename=NULL,
                    lwd=2,#圈线粗度
                    lty=1, #圈线类型
                    fill=c('red',"blue","yellow"), #填充颜色
                    col=c('red',"blue","yellow"), #圈线颜色
                    cat.col=c('red',"blue","yellow"),#A和B的颜色
                    cat.cex = 4,# A和B的大小
                    rotation.degree = 0,#旋转角度
                    main = "",#主标题内容
                    main.cex = 2,#主标题大小
                    sub = "",#亚标题内容
                    sub.cex = 1,#亚标题字大小
                    cex=3,#里面交集字的大小
                    alpha = 0.5,#透明度
                    reverse=TRUE,
                    scaled     = FALSE)
    grid::grid.draw(a)
    dev.off()
    filename33 = paste(path,"ven",".jpg",sep = "",collapse="_")
    jpeg(file=filename33)
    grid::grid.draw(a)
    dev.off()
    grid::grid.draw(a)
  } else if (length(names(ven3)) == 4) {
    filename3 = paste(path,"ven_",paste(names(ven3),sep = "",collapse="-"),".pdf",sep = "",collapse="_")
    pdf(file=filename3,width = 18, height = 15)
    a<-VennDiagram::venn.diagram(ven3,
                    filename=NULL,
                    lwd=2,#圈线粗度
                    lty=1, #圈线类型
                    fill=c('red',"blue","yellow","#7ad2f6"), #填充颜色
                    col=c('red',"blue","yellow","#7ad2f6"), #圈线颜色
                    cat.col=c('red',"blue","yellow","#7ad2f6"),#A和B的颜色
                    cat.cex = 4,# A和B的大小
                    rotation.degree = 0,#旋转角度
                    main = "",#主标题内容
                    main.cex = 2,#主标题大小
                    sub = "",#亚标题内容
                    sub.cex = 1,#亚标题字大小
                    cex=3,#里面交集字的大小
                    alpha = 0.5,#透明度
                    reverse=TRUE,
                    scaled     = FALSE)
    grid::grid.draw(a)
    dev.off()
    filename33 = paste(path,"ven",".jpg",sep = "",collapse="_")
    jpeg(file=filename33)
    grid::grid.draw(a)
    dev.off()
    grid::grid.draw(a)
  }else if (length(names(ven3)) == 5) {
    filename3 = paste(path,"ven_",paste(names(ven3),sep = "",collapse="-"),".pdf",sep = "",collapse="_")
    pdf(file=filename3,width = 12, height = 12)
    a<- VennDiagram::venn.diagram(ven3,
                    filename=NULL,
                    lwd=2,#圈线粗度
                    lty=1, #圈线类型
                    fill=c('red',"blue","yellow","#7ad2f6","green"), #填充颜色
                    col=c('red',"blue","yellow","#7ad2f6","green"), #圈线颜色
                    cat.col=c('red',"blue","yellow","#7ad2f6","green"),#A和B的颜色
                    cat.cex = 4,# A和B的大小
                    rotation.degree = 0,#旋转角度
                    main = "",#主标题内容
                    main.cex = 2,#主标题大小
                    sub = "",#亚标题内容
                    sub.cex = 1,#亚标题字大小
                    cex=3,#里面交集字的大小
                    alpha = 0.5,#透明度
                    reverse=TRUE,
                    scaled     = FALSE)
    grid::grid.draw(a)
    dev.off()
    filename33 = paste(path,"ven",".jpg",sep = "",collapse="_")
    jpeg(file=filename33)
    grid::grid.draw(a)
    dev.off()
    grid::grid.draw(a)
  }else if (length(names(ven3)) == 6) {
    
    print("ven not use for more than 6")
  }

Venn-network diagram(维恩网络图)


library(ggClusterNet)
library(phyloseq)
biospath = paste(otupath,"/biospr_network_Ven/",sep = "")
dir.create(biospath)
N = 0.5
result = ggClusterNet::div_network(ps)
edge = result[[1]]
data = result[[3]]


result <- ggClusterNet::div_culculate(table = result[[3]],distance = 1.1,distance2 = 1.5,distance3 = 1.3,order = FALSE)
# result <- div_culculate(table = result[[3]],distance = 1,distance2 = 1.2,distance3 = 1.1,order = FALSE)
edge = result[[1]]

plotdata = result[[2]]

#--这部分数据是样本点数据
groupdata <- result[[3]]
# table(plotdata$elements)
node =  plotdata[plotdata$elements == unique(plotdata$elements), ]

otu_table = as.data.frame(t(ggClusterNet::vegan_otu(ps)))
tax_table = as.data.frame(ggClusterNet::vegan_tax(ps))
res = merge(node,tax_table,by = "row.names",all = F)
row.names(res) = res$Row.names
res$Row.names = NULL
plotcord = res

xx = data.frame(mean  =rowMeans(otu_table))

plotcord = merge(plotcord,xx,by = "row.names",all = FALSE)
head(plotcord)
# plotcord$Phylum
row.names(plotcord) = plotcord$Row.names
plotcord$Row.names = NULL
head(plotcord)
library(ggrepel)
head(plotcord)
head(groupdata)
p = ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2),
                            data = edge, size = 0.3,color = "yellow") +
  geom_point(aes(X1, X2,fill = Phylum,size =mean ),pch = 21, data = plotcord) +
  geom_point(aes(X1, X2),pch = 21, data = groupdata,size = 5,fill = "blue",color = "black") +
  geom_text(aes(X1, X2,label = elements ), data = groupdata,hjust = 1,vjust = -1) +
  theme_void()

p


filename = paste(biospath,"/","biostr_Ven_network.pdf",sep = "")
ggsave(filename,p,width = (15),height = (12))
filename = paste(biospath,"/","biostr_Ven_network.jpg",sep = "")
ggsave(filename,p,width = (15),height = (12))

detach("package:ggClusterNet")
detach("package:phyloseq")

Sankey diagram(桑基图)

# BiocManager::install("networkD3")
# BiocManager::install("webshot")

library(ggClusterNet)
library(phyloseq)
snapath =  paste(otupath,"/sankeyNetwork/",sep = "");otupath
#> [1] ".//result_and_plot//Base_diversity_16s/OTU/"
dir.create(snapath)

ps = readRDS("./data/dataNEW/ps_16s.rds")

rank=6#参数目前不可修改
Top=50
snapath=snapath

map = sample_data(ps) %>% as.tibble()
otu =  ps %>% vegan_otu() %>% 
  as.data.frame()
  otu$ID = row.names(otu)
  
  tax = ps %>% 
    scale_micro() %>%
    vegan_tax() %>% 
    as.data.frame()
  tax$taxid = row.names(tax)
  head(tax)
  

  data = map %>% as.tibble() %>%
    inner_join(otu) %>% 
    gather( taxa, count, starts_with("ASV_")) %>%
    inner_join(tax,by = c("taxa" = "taxid") )
  data
  
  
  tax = ps %>% 
    ggClusterNet::tax_glom_wt(ranks = rank) %>%
    ggClusterNet::filter_OTU_ps(Top) %>%
    subset_taxa(!Genus  %in% c("Unassigned","Unknown")) %>%
    ggClusterNet::vegan_tax() %>%
    as.data.frame()
  head(tax)
  dim(tax)
#> [1] 49  6
  id2 = c("k","p","c","o","f","g")
  dat = NULL
  for (i in 1:5) {
    dat <- tax[,c(i,i+1)] %>% distinct(.keep_all = TRUE) 
    colnames(dat) = c("source","target")
    dat$source = paste(id2[i],dat$source,sep = "_")
    dat$target = paste(id2[i+1],dat$target,sep = "_")
    if (i == 1) {
      dat2 = dat
    }  
    
    dat2 = rbind(dat2,dat)
    
  }
  dim(dat2)
#> [1] 169   2
  # dat2 = dat2 %>% distinct(.keep_all = TRUE) 
  
  head(dat2)
  
  otu = ps %>% 
    ggClusterNet::tax_glom_wt(ranks = 6) %>%
    ggClusterNet::scale_micro() %>%
    ggClusterNet::filter_OTU_ps(Top) %>%
    subset_taxa(!Genus  %in% c("Unassigned","Unknown")) %>%
    ggClusterNet::vegan_otu() %>%
    t() %>%
    as.data.frame()
  
  head(otu)
  otutax = cbind(otu,tax)
  
  id = rank.names(ps)[1:6]
  dat = NULL
  dat3 = NULL
  head(data)
  
  for (j in 1) {
    dat = data %>% group_by(Group,!!sym(rank_names(ps)[j])) %>%
      summarise_if(is.numeric,sum,na.rm = TRUE)
    colnames(dat) = c("source","target","value")
    dat$target = paste(id2[j],dat$target,sep = "_")
    
    if (j == 1) {
      dat3 = dat
    }  
    # dat3 = rbind(dat3,dat)
  }
  
  
  dat3 %>% tail()
  tem = data.frame(target = dat3$target,value = dat3$value)
  dat3$value = NULL
  dat2 = rbind(dat2,dat3)
  
  
  head( otutax)
  for (i in 1:6) {
    dat <- otutax %>%
      dplyr::group_by(!!sym(id[i])) %>%
      summarise_if(is.numeric,sum,na.rm = TRUE)
    
    dat = data.frame(Genus = dat[,1],value =rowSums(dat[,-1]) )
    colnames(dat) = c("target","value")
    dat$target = paste(id2[i],dat$target,sep = "_")
    if (i == 1) {
      dat3 = dat
    }  
    
    dat3 = rbind(dat3,dat)
  }
  
  dat4 <- dat2 %>% left_join(dat3)
  sankey = dat4
  
  head( sankey )
  
  
  nodes <- data.frame(name = unique(c(as.character(sankey$source),as.character(sankey$target))),stringsAsFactors = FALSE)
  nodes$ID <- 0:(nrow(nodes)-1)
  sankey <- merge(sankey,nodes,by.x = "source",by.y = "name")
  sankey <- merge(sankey,nodes,by.x = "target",by.y = "name")
  colnames(sankey) <- c("X","Y","value","source","target")
  sankey <- subset(sankey,select = c("source","target","value"))
  nodes <- subset(nodes,select = c("name"))
  
  ColourScal='d3.scaleOrdinal() .range(["#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#FFFF33", "#A65628", "#F781BF", "#999999"])'
  
  sankey$energy_type <- sub(' .*', '', nodes[sankey$source + 1, 'name'])
  library(networkD3)
  p <- sankeyNetwork(Links = sankey, Nodes = nodes,
                     Source = "source",Target = "target",Value = "value",
                     NodeID = "name",
                     sinksRight=FALSE,
                     LinkGroup = 'energy_type',
                     colourScale= ColourScal, 
                     # nodeWidth=40,
                     # fontSize=13
                     # nodePadding=20
  )

# return(list(p,sankey))

dat = sankey

FileName <-paste(snapath,"/sankey_Group.csv", sep = "")
write.csv(dat,FileName,sep = "")

saveNetwork(p,paste(snapath,"/sankey_Group.html", sep = ""))
library(webshot)
# webshot::install_phantomjs()
# webshot(paste(snapath,"/sankey1.html", sep = "") ,paste(snapath,"/sankey1.png", sep = ""))
webshot(paste(snapath,"/sankey_Group.html", sep = "") , paste(snapath,"/sankey_Group.pdf", sep = ""))

2.Difference analysis (2.差异分析)

edgeR and DESep2-Volcano plot(edgeR和DESep2-火山图)


ps = readRDS("./data/dataNEW/ps_16s.rds")

diffpath = paste(otupath,"/diff_tax/",sep = "")
dir.create(diffpath)

diffpath.1 = paste(diffpath,"/DEsep2/",sep = "")
dir.create(diffpath.1)

diffpath.2 = diffpath.1
# 准备脚本
group  = "Group"
pvalue = 0.05
lfc =0
artGroup = NULL
method = "TMM"
j = 2
path = diffpath
b = NULL


  if (j %in% c("OTU","gene","meta")) {
    ps = ps 
  } else if (j %in% c(1:7)) {
    ps = ps %>% 
      ggClusterNet::tax_glom_wt(ranks = j)
  } else if (j %in% c("Kingdom","Phylum","Class","Order","Family","Genus","Species")){
    
  } else {
    ps = ps
    print("unknown j, checked please")
  }
  sub_design <- as.data.frame(phyloseq::sample_data(ps))

  Desep_group <- as.character(levels(as.factor(sub_design$Group)))
  Desep_group
#> [1] "Group1" "Group2" "Group3"
  
  if ( is.null(artGroup)) {
    #--构造两两组合#-----
    aaa = combn(Desep_group,2)
    # sub_design <- as.data.frame(sample_data(ps))
  }
  if (!is.null(artGroup)) {
    aaa  = as.matrix(b)
  }
  otu_table = as.data.frame(ggClusterNet::vegan_otu(ps))
  count = as.matrix(otu_table)
  count <- t(count)
  sub_design <- as.data.frame(phyloseq::sample_data(ps))
  dim(sub_design)
#> [1] 18  2
  sub_design$SampleType = as.character(sub_design$Group)
  sub_design$SampleType <- as.factor(sub_design$Group)
  # create DGE list
  d = edgeR::DGEList(counts=count, group=sub_design$SampleType)
  d$samples
  d = edgeR::calcNormFactors(d,method=method)#默认为TMM标准化

  # Building experiment matrix
  design.mat = model.matrix(~ 0 + d$samples$group)
  colnames(design.mat)=levels(sub_design$SampleType)
  d2 = edgeR::estimateGLMCommonDisp(d, design.mat)
  d2 = edgeR::estimateGLMTagwiseDisp(d2, design.mat)
  fit = edgeR::glmFit(d2, design.mat)

  #------------根据分组提取需要的差异结果#------------
  for (i in 1:dim(aaa)[2]) {
    # i = 1
    Desep_group = aaa[,i]
    print( Desep_group)

    # head(design)
    # 设置比较组写在前面的分组为enrich表明第一个分组含量高
# ?limma::makeContrasts
    group = paste(Desep_group[1],Desep_group[2],sep = "-")
    group
    BvsA <- limma::makeContrasts(contrasts =  group,levels=c( as.character(levels(as.factor(sub_design$Group)))) )#注意是以GF1为对照做的比较
    # 组间比较,统计Fold change, Pvalue
    lrt = edgeR::glmLRT(fit,contrast=BvsA)

    # FDR检验,控制假阳性率小于5%
    de_lrt = edgeR::decideTestsDGE(lrt, adjust.method="fdr", p.value=pvalue,lfc=lfc)#lfc=0这个是默认值
    summary(de_lrt)
    # 导出计算结果
    x=lrt$table
    x$sig=de_lrt
    head(x)
    #------差异结果符合otu表格的顺序
    row.names(count)[1:6]

    x <- cbind(x, padj = p.adjust(x$PValue, method = "fdr"))
    enriched = row.names(subset(x,sig==1))
    depleted = row.names(subset(x,sig==-1))

    x$level = as.factor(ifelse(as.vector(x$sig) ==1, "enriched",ifelse(as.vector(x$sig)==-1, "depleted","nosig")))
    x = data.frame(row.names = row.names(x),logFC = x$logFC,level = x$level,p = x$PValue)
    head(x)
    # colnames(x) = paste(group,colnames(x),sep = "")
    # x = res
    # head(x)
    #------差异结果符合otu表格的顺序
    # x = data.frame(row.names = row.names(x),logFC = x$log2FoldChange,level = x$level,p = x$pvalue) 
    x1 = x %>%
      dplyr::filter(level %in% c("enriched","depleted","nosig") )
    head(x1)
    x1$Genus = row.names(x1)
    # x$level = factor(x$level,levels = c("enriched","depleted","nosig"))
    if (nrow(x1)<= 1) {
      
    }
    x2 <- x1 %>% 
      dplyr::mutate(ord = logFC^2) %>%
      dplyr::filter(level != "nosig") %>%
      dplyr::arrange(desc(ord)) %>%
      head(n = 5)
    
    file = paste(path,"/",group,j,"_","Edger_Volcano_Top5.csv",sep = "")
    write.csv(x2,file,quote = F)
    head(x2)
    
    p <- ggplot(x1,aes(x =logFC ,y = -log2(p), colour=level)) +
      geom_point() +
      geom_hline(yintercept=-log10(0.2),
                 linetype=4,
                 color = 'black',
                 size = 0.5) +
      geom_vline(xintercept=c(-1,1),
                 linetype=3,
                 color = 'black',
                 size = 0.5) +
      ggrepel::geom_text_repel(data=x2, aes(x =logFC ,y = -log2(p), label=Genus), size=1) +
      scale_color_manual(values = c('blue2','red2', 'gray30')) + 
      ggtitle(group) + theme_bw()
    
    p
    
    file = paste(path,"/",group,j,"_","Edger_Volcano.pdf",sep = "")
    ggsave(file,p,width = 8,height = 6)
    
    file = paste(path,"/",group,j,"_","Edger_Volcano.png",sep = "")
    ggsave(file,p,width = 8,height = 6)
    
    
    colnames(x) = paste(group,colnames(x),sep = "")
    
    

    if (i ==1) {
      table =x
    }
    if (i != 1) {
      table = cbind(table,x)
    }
  }
#> [1] "Group1" "Group2"
#> [1] "Group1" "Group3"
#> [1] "Group2" "Group3"

  x = table

  ###########添加物种丰度#----------
  # dim(count)
  # str(count)
  count = as.matrix(count)
  norm = t(t(count)/colSums(count)) #* 100 # normalization to total 100
  dim(norm)
#> [1] 39 18
  norm1 = norm %>%
    t() %>% as.data.frame()
  # head(norm1)
  #数据分组计算平均值
  library("tidyverse")
  head(norm1)

  iris.split <- split(norm1,as.factor(sub_design$SampleType))
  iris.apply <- lapply(iris.split,function(x)colMeans(x))
  # 组合结果
  iris.combine <- do.call(rbind,iris.apply)
  norm2= t(iris.combine)

  #head(norm)
  str(norm2)
#>  num [1:39, 1:3] 1.56e-01 3.58e-02 2.84e-06 8.69e-03 6.02e-02 ...
#>  - attr(*, "dimnames")=List of 2
#>   ..$ : chr [1:39] "Acidobacteria" "Actinobacteria" "Aminicenantes" "Armatimonadetes" ...
#>   ..$ : chr [1:3] "Group1" "Group2" "Group3"
  norm2 = as.data.frame(norm2)
  # dim(x)
  head(norm2)
  x = cbind(x,norm2)
  head(x)
  #在加入这个文件taxonomy时,去除后面两列不相干的列
  # 读取taxonomy,并添加各列名称

  if (!is.null(ps@tax_table)) {
    taxonomy = as.data.frame(ggClusterNet::vegan_tax(ps))
    head(taxonomy)
    # taxonomy <- as.data.frame(tax_table(ps1))

    #发现这个注释文件并不适用于直接作图。
    #采用excel将其分列处理,并且删去最后一列,才可以运行
    if (length(colnames(taxonomy)) == 6) {
      colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus")
    }else if (length(colnames(taxonomy)) == 7) {
      colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus","species")
    }else if (length(colnames(taxonomy)) == 8) {
      colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus","species","rep")
    }
    # colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus")

    # Taxonomy排序,并筛选OTU表中存在的
    library(dplyr)
    taxonomy$id=rownames(taxonomy)
    # head(taxonomy)
    tax = taxonomy[row.names(x),]
    x = x[rownames(tax), ] # reorder according to tax

    if (length(colnames(taxonomy)) == 7) {
      x = x[rownames(tax), ] # reorder according to tax
      x$phylum = gsub("","",tax$phylum,perl=TRUE)
      x$class = gsub("","",tax$class,perl=TRUE)
      x$order = gsub("","",tax$order,perl=TRUE)
      x$family = gsub("","",tax$family,perl=TRUE)
      x$genus = gsub("","",tax$genus,perl=TRUE)
      # x$species = gsub("","",tax$species,perl=TRUE)
    }else if (length(colnames(taxonomy)) == 8) {
      x$phylum = gsub("","",tax$phylum,perl=TRUE)
      x$class = gsub("","",tax$class,perl=TRUE)
      x$order = gsub("","",tax$order,perl=TRUE)
      x$family = gsub("","",tax$family,perl=TRUE)
      x$genus = gsub("","",tax$genus,perl=TRUE)
      x$species = gsub("","",tax$species,perl=TRUE)
    }else if (length(colnames(taxonomy)) == 9) {
      x = x[rownames(tax), ] # reorder according to tax
      x$phylum = gsub("","",tax$phylum,perl=TRUE)
      x$class = gsub("","",tax$class,perl=TRUE)
      x$order = gsub("","",tax$order,perl=TRUE)
      x$family = gsub("","",tax$family,perl=TRUE)
      x$genus = gsub("","",tax$genus,perl=TRUE)
      x$species = gsub("","",tax$species,perl=TRUE)

    }

  } else {
    x = cbind(x,tax)
  }


res = x
head(res) 
filename = paste(diffpath.2,"/","_",j,"_","edger_all.csv",sep = "")
write.csv(res,filename)

                        j = "Genus"
                        group  = "Group"
                        pvalue = 0.05
                        artGroup = NULL
                        path = diffpath

  

  # ps = ps %>% 
  #   ggClusterNet::tax_glom_wt(ranks = j)
  if (j %in% c("OTU","gene","meta")) {
    ps = ps 
  } else if (j %in% c(1:7)) {
    ps = ps %>% 
      ggClusterNet::tax_glom_wt(ranks = j)
  } else if (j %in% c("Kingdom","Phylum","Class","Order","Family","Genus","Species")){
    
  } else {
    ps = ps
    print("unknown j, checked please")
  }
#> NULL
  
  
  Desep_group <- ps %>% 
    phyloseq::sample_data() %>%
    .$Group %>%
    as.factor() %>%
    levels() %>%
    as.character()
  
  if ( is.null(artGroup)) {
    #--构造两两组合#-----
    aaa = combn(Desep_group,2)
    # sub_design <- as.data.frame(sample_data(ps))
  } else if (!is.null(artGroup)) {
    aaa  = as.matrix(b )
  }
  
  count <-  ps %>% 
    ggClusterNet::vegan_otu() %>% round(0) %>%
    t() 
  
  
  map = ps %>% 
    phyloseq::sample_data() %>%
    as.tibble() %>%
    as.data.frame()
  
  
  dds <- DESeq2::DESeqDataSetFromMatrix(countData = count,
                                colData = map,
                                design = ~ Group)
  
  dds2 <- DESeq2::DESeq(dds)  ##第二步,标准化
  # resultsNames(dds2)
  
  
  #------------根据分组提取需要的差异结果#------------
  for (i in 1:dim(aaa)[2]) {
    # i = 1
    Desep_group = aaa[,i]
    print( Desep_group)
    
    
    # head(design)
    # 设置比较组写在前面的分组为enrich表明第一个分组含量高
    
    group = paste(Desep_group[1],Desep_group[2],sep = "-")
    group
    
    # 将结果用results()函数来获取,赋值给res变量
    res <-  DESeq2::results(dds2, contrast=c("Group",Desep_group ),alpha=0.05)
    # 导出计算结果
    
    x = res
    head(x)
    #------差异结果符合otu表格的顺序
    
    x$level = as.factor(ifelse(as.vector(x$padj) < 0.05 & x$log2FoldChange > 0, "enriched",
                               ifelse(as.vector(x$padj) < 0.05 &x$log2FoldChange < 0, "depleted","nosig")))
    
    
    x = data.frame(row.names = row.names(x),logFC = x$log2FoldChange,level = x$level,p = x$pvalue) 
    x1 = x %>%
      filter(level %in% c("enriched","depleted","nosig") )
    head(x1)
    x1$Genus = row.names(x1)
    # x$level = factor(x$level,levels = c("enriched","depleted","nosig"))
    
    x2 <- x1 %>% 
      dplyr::mutate(ord = logFC^2) %>%
      dplyr::filter(level != "nosig") %>%
      dplyr::arrange(desc(ord)) %>%
      head(n = 5)
    
    file = paste(path,"/",group,j,"_","DESep2_Volcano_Top5.csv",sep = "")
    write.csv(x2,file,quote = F)
    head(x2)
    
    p <- ggplot(x1,aes(x =logFC ,y = -log2(p), colour=level)) +
      geom_point() +
      geom_hline(yintercept=-log10(0.2),
                 linetype=4,
                 color = 'black',
                 size = 0.5) +
      geom_vline(xintercept=c(-1,1),
                 linetype=3,
                 color = 'black',
                 size = 0.5) +
      ggrepel::geom_text_repel(data=x2, aes(x =logFC ,y = -log2(p), label=Genus), size=1) +
      scale_color_manual(values = c('blue2','red2', 'gray30')) + 
      ggtitle(group) + theme_bw()
    
    p
    
    file = paste(path,"/",group,j,"_","DESep2_Volcano.pdf",sep = "")
    ggsave(file,p,width = 8,height = 6)
    
    file = paste(path,"/",group,j,"_","DESep2_Volcano.png",sep = "")
    ggsave(file,p,width = 8,height = 6)
    
    colnames(x) = paste(group,colnames(x),sep = "")
    
    
    if (i ==1) {
      table =x
    }
    if (i != 1) {
      table = cbind(table,x)
    }
  }
#> [1] "Group1" "Group2"
#> [1] "Group1" "Group3"
#> [1] "Group2" "Group3"
  

  count = as.matrix(count)
  norm = t(t(count)/colSums(count)) #* 100 # normalization to total 100
  dim(norm)
#> [1] 39 18
  norm1 = norm %>%
    t() %>% as.data.frame()
  # head(norm1)
  #数据分组计算平均值
  # library("tidyverse")
  head(norm1)
  
  iris.split <- split(norm1,as.factor(map$Group))
  iris.apply <- lapply(iris.split,function(x)colMeans(x))
  # 组合结果
  iris.combine <- do.call(rbind,iris.apply)
  norm2= t(iris.combine)
  
  #head(norm)
  str(norm2)
#>  num [1:39, 1:3] 1.56e-01 3.58e-02 2.84e-06 8.69e-03 6.02e-02 ...
#>  - attr(*, "dimnames")=List of 2
#>   ..$ : chr [1:39] "Acidobacteria" "Actinobacteria" "Aminicenantes" "Armatimonadetes" ...
#>   ..$ : chr [1:3] "Group1" "Group2" "Group3"
  norm2 = as.data.frame(norm2)
  
  head(norm2)
  x = cbind(table,norm2)
  head(x)
  #在加入这个文件taxonomy时,去除后面两列不相干的列
  # 读取taxonomy,并添加各列名称
  
  if (!is.null(ps@tax_table)) {
    taxonomy = as.data.frame(ggClusterNet::vegan_tax(ps))
    head(taxonomy)
    # taxonomy <- as.data.frame(tax_table(ps1))
    
    #发现这个注释文件并不适用于直接作图。
    #采用excel将其分列处理,并且删去最后一列,才可以运行
    if (length(colnames(taxonomy)) == 6) {
      colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus")
    }else if (length(colnames(taxonomy)) == 7) {
      colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus","species")
    }else if (length(colnames(taxonomy)) == 8) {
      colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus","species","rep")
    }
    # colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus")
    
    # Taxonomy排序,并筛选OTU表中存在的
    library(dplyr)
    taxonomy$id=rownames(taxonomy)
    # head(taxonomy)
    tax = taxonomy[row.names(x),]
    x = x[rownames(tax), ] # reorder according to tax
    
    if (length(colnames(taxonomy)) == 7) {
      x = x[rownames(tax), ] # reorder according to tax
      x$phylum = gsub("","",tax$phylum,perl=TRUE)
      x$class = gsub("","",tax$class,perl=TRUE)
      x$order = gsub("","",tax$order,perl=TRUE)
      x$family = gsub("","",tax$family,perl=TRUE)
      x$genus = gsub("","",tax$genus,perl=TRUE)
      # x$species = gsub("","",tax$species,perl=TRUE)
    }else if (length(colnames(taxonomy)) == 8) {
      x$phylum = gsub("","",tax$phylum,perl=TRUE)
      x$class = gsub("","",tax$class,perl=TRUE)
      x$order = gsub("","",tax$order,perl=TRUE)
      x$family = gsub("","",tax$family,perl=TRUE)
      x$genus = gsub("","",tax$genus,perl=TRUE)
      x$species = gsub("","",tax$species,perl=TRUE)
    }else if (length(colnames(taxonomy)) == 9) {
      x = x[rownames(tax), ] # reorder according to tax
      x$phylum = gsub("","",tax$phylum,perl=TRUE)
      x$class = gsub("","",tax$class,perl=TRUE)
      x$order = gsub("","",tax$order,perl=TRUE)
      x$family = gsub("","",tax$family,perl=TRUE)
      x$genus = gsub("","",tax$genus,perl=TRUE)
      x$species = gsub("","",tax$species,perl=TRUE)
      
      
    }
    
  } else {
    x = x
  }
  





res = x
head(res)

filename = paste(diffpath.1,"/","_",j,"_","DESep2_all.csv",sep = "")
write.csv(res,filename,quote = F)

edgeR-Manhattan diagram(edgeR-曼哈顿图)


diffpath = paste(otupath,"/diff_Manhattan/",sep = "")
dir.create(diffpath)

  ps = ps
  pvalue = 0.05
  lfc = 0
  diffpath = diffpath 


count = ps %>% 
    ggClusterNet::vegan_otu() %>%
    t()
# create DGE list

  d = edgeR::DGEList(counts=count, group= phyloseq::sample_data(ps)$Group)
  d = edgeR::calcNormFactors(d)
  design.mat = model.matrix(~ 0 + d$samples$group)
  colnames(design.mat)=levels(as.factor(phyloseq::sample_data(ps)$Group))
  d2 = edgeR::estimateGLMCommonDisp(d, design.mat)
  d2 = edgeR::estimateGLMTagwiseDisp(d2, design.mat)
  fit = edgeR::glmFit(d2, design.mat)
  
  Desep_group <- as.character(levels(as.factor(phyloseq::sample_data(ps)$Group)))
  Desep_group
#> [1] "Group1" "Group2" "Group3"
  
  aaa = combn(Desep_group,2)
  
  for (i in 1:dim(aaa)[2]) {
    # i = 1
    Desep_group = aaa[,i]
    print( Desep_group)
    group = paste(Desep_group[1],Desep_group[2],sep = "-")
    group
    BvsA <- limma::makeContrasts(contrasts =  group,levels=design.mat)#注意是以GF1为对照做的比较
    # 组间比较,统计Fold change, Pvalue
    lrt = edgeR::glmLRT(fit,contrast=BvsA)
    
    # FDR检验,控制假阳性率小于5%
    de_lrt = edgeR::decideTestsDGE(lrt, adjust.method="fdr", p.value=pvalue,lfc=lfc)#lfc=0这个是默认值
    summary(de_lrt)
    # 导出计算结果
    x=lrt$table
    x$sig=de_lrt
    
    x$sig=de_lrt
    head(x)
    #------差异结果符合otu表格的顺序
    row.names(count)[1:6]
    
    x <- cbind(x, padj = p.adjust(x$PValue, method = "fdr"))
    enriched = row.names(subset(x,sig==1))
    depleted = row.names(subset(x,sig==-1))
    x$level = as.factor(ifelse(as.vector(x$sig) ==1, "enriched",ifelse(as.vector(x$sig)==-1, "depleted","nosig")))
    x$otu = rownames(x)
    x$neglogp = -log(x$PValue)

    tax = ps %>% 
      ggClusterNet::vegan_tax() %>%
      as.data.frame()
    head(tax)
    x = cbind(x,tax)
    head(x)
    
    top_phylum=c("Bacteroidetes","Firmicutes","Planctomycetes","Proteobacteria","Verrucomicrobia")
    x[!(x$Phylum %in% top_phylum),]$Phylum = "Low Abundance" # no level can get value
    x$otu = factor(x$otu, levels=x$otu)   # set x order
    x$level = factor(x$level, levels=c("enriched","depleted","nosig"))
    levels(x$Phylum)=c(top_phylum,"Low Abundance")
    x = x %>% arrange(Phylum)
    head(x)
    x$otu = factor(x$otu, levels=x$otu)   
    
    # if (tem != 0) {
    #   tem = x[x$neglogp>15,] %>% nrow()
    # }
    
    FDR = min(x$neglogp[x$level=="depleted"])
    p = ggplot(x, aes(x=otu, y=neglogp, color=Phylum, size=logCPM, shape=level)) +
      geom_point(alpha=.7) + 
      geom_hline(yintercept=FDR, linetype=2, color="lightgrey") +
      scale_shape_manual(values=c(17, 25, 20))+
      scale_size(breaks=c(5, 10, 15)) +
      labs(x="OTU", y="-loge(P)") +
      theme(axis.ticks.x=element_blank(),axis.text.x=element_blank(),legend.position="top") +
      scale_color_manual(values = c(RColorBrewer::brewer.pal(9,"Set1")))
    p
    
    filename = paste(diffpath,"/",paste(Desep_group[1],Desep_group[2],sep = "_"),"Manhattan_plot.pdf",sep = "")
    ggsave(filename,p,width = 16,height = 6)
    filename = paste(diffpath,"/",paste(Desep_group[1],Desep_group[2],sep = "_"),"Manhattan_plot.png",sep = "")
    ggsave(filename,p,width = 16,height = 6,dpi = 72)
    
  }
#> [1] "Group1" "Group2"
#> [1] "Group1" "Group3"
#> [1] "Group2" "Group3"

STAMP_difference analysis(STAMP_差异分析)


ps = readRDS("./data/dataNEW/ps_16s.rds")
diffpath = paste(otupath,"/stemp_diff/",sep = "")
dir.create(diffpath)

allgroup <- combn(unique(map$Group),2)
ps_sub <- phyloseq::subset_samples(ps,Group %in% allgroup[,1]);ps_sub
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:         [ 37178 taxa and 12 samples ]
#> sample_data() Sample Data:       [ 12 samples by 2 sample variables ]
#> tax_table()   Taxonomy Table:    [ 37178 taxa by 7 taxonomic ranks ]
#> phy_tree()    Phylogenetic Tree: [ 37178 tips and 37176 internal nodes ]
#> refseq()      DNAStringSet:      [ 37178 reference sequences ]
Top = 20
ranks = 6
method = "TMM"
test.method = "t.test"


#--门水平合并
data   = ggClusterNet::tax_glom_wt(ps_sub,ranks = phyloseq::rank_names(ps)[ranks]) %>%
    ggClusterNet::scale_micro(method = method) %>%
    ggClusterNet::filter_OTU_ps(Top = 200) %>%
    ggClusterNet::vegan_otu() %>%
    as.data.frame()
  tem = colnames(data)
  data$ID = row.names(data)

  data <- data %>%
    dplyr::inner_join(as.tibble(phyloseq::sample_data(ps)),by = "ID")
  data$Group = as.factor(data$Group)

  

  if (test.method == "t.test") {
    diff <- data[,tem] %>%
      # dplyr::select_if(is.numeric) %>%
      purrr::map_df(~ broom::tidy(t.test(. ~ Group,data = data)), .id = 'var')

  } else if(test.method == "wilcox.test"){
    diff <- data[,tem] %>%
      # dplyr::select_if(is.numeric) %>%
      purrr::map_df(~ broom::tidy(wilcox.test(. ~ Group,data = data)), .id = 'var')
    
  }
  
  diff$p.value[is.nan(diff$p.value)] = 1
  diff$p.value <- p.adjust(diff$p.value,"bonferroni")
  tem = diff$p.value [diff$p.value < 0.05] %>% length()
  if (tem > 30) {
    diff <- diff %>% 
      dplyr::filter(p.value < 0.05) %>%
      head(30)
  } else {
    diff <- diff %>% 
      # filter(p.value < 0.05) %>%
      head(30)
  }
  
  # diff <- diff %>% filter(p.value < 0.05)

  # diff1$p.value <- p.adjust(diff1$p.value,"bonferroni")
  # diff1 <- diff1 %>% filter(p.value < 0.05)
  
  abun.bar <- data[,c(diff$var,"Group")] %>%
    tidyr::gather(variable,value,-Group) %>%
    dplyr::group_by(variable,Group) %>%
    dplyr::summarise(Mean = mean(value))
  
  
  diff.mean <- diff[,c("var","estimate","conf.low","conf.high","p.value")]
  diff.mean$Group <- c(ifelse(diff.mean$estimate >0,levels(data$Group)[1],
                              levels(data$Group)[2]))
  diff.mean <- diff.mean[order(diff.mean$estimate,decreasing = TRUE),]
  
  
  cbbPalette <- c("#E69F00", "#56B4E9")
  abun.bar$variable <- factor(abun.bar$variable,levels = rev(diff.mean$var))
  
  
  p1 <- ggplot(abun.bar,aes(variable,Mean,fill = Group)) +
    scale_x_discrete(limits = levels(diff.mean$var)) +
    coord_flip() +
    xlab("") +
    ylab("Mean proportion (%)") +
    theme(panel.background = element_rect(fill = 'transparent'),
          panel.grid = element_blank(),
          axis.ticks.length = unit(0.4,"lines"),
          axis.ticks = element_line(color='black'),
          axis.line = element_line(colour = "black"),
          axis.title.x=element_text(colour='black', size=12,face = "bold"),
          axis.text=element_text(colour='black',size=10,face = "bold"),
          legend.title=element_blank(),
          # legend.text=element_text(size=12,face = "bold",colour = "black",
          #                          margin = margin(r = 20)),
          legend.position = c(-1,-0.1),
          legend.direction = "horizontal",
          legend.key.width = unit(0.8,"cm"),
          legend.key.height = unit(0.5,"cm"))
  
  p1

  
  for (i in 1:(nrow(diff.mean) - 1))
    p1 <- p1 + annotate('rect', xmin = i+0.5, xmax = i+1.5, ymin = -Inf, ymax = Inf,
                        fill = ifelse(i %% 2 == 0, 'white', 'gray95'))
  
  p1

  p1 <- p1 +
    geom_bar(stat = "identity",position = "dodge",width = 0.7,colour = "black") +
    scale_fill_manual(values=cbbPalette) + theme(legend.position = "bottom")
  p1

  
  diff.mean$var <- factor(diff.mean$var,levels = levels(abun.bar$variable))
  diff.mean$p.value <- signif(diff.mean$p.value,3)
  diff.mean$p.value <- as.character(diff.mean$p.value)
  
  p2 <- ggplot(diff.mean,aes(var,estimate,fill = Group)) +
    theme(panel.background = element_rect(fill = 'transparent'),
          panel.grid = element_blank(),
          axis.ticks.length = unit(0.4,"lines"),
          axis.ticks = element_line(color='black'),
          axis.line = element_line(colour = "black"),
          axis.title.x=element_text(colour='black', size=12,face = "bold"),
          axis.text=element_text(colour='black',size=10,face = "bold"),
          axis.text.y = element_blank(),
          legend.position = "none",
          axis.line.y = element_blank(),
          axis.ticks.y = element_blank(),
          plot.title = element_text(size = 15,face = "bold",colour = "black",hjust = 0.5)) +
    scale_x_discrete(limits = levels(diff.mean$var)) +
    coord_flip() +
    xlab("") +
    ylab("Difference in mean proportions (%)") +
    labs(title="95% confidence intervals")
  
  for (i in 1:(nrow(diff.mean) - 1))
    p2 <- p2 + annotate('rect', xmin = i+0.5, xmax = i+1.5, ymin = -Inf, ymax = Inf,
                        fill = ifelse(i %% 2 == 0, 'white', 'gray95'))
  
  p2 <- p2 +
    geom_errorbar(aes(ymin = conf.low, ymax = conf.high),
                  position = position_dodge(0.8), width = 0.5, size = 0.5) +
    geom_point(shape = 21,size = 3) +
    scale_fill_manual(values=cbbPalette) +
    geom_hline(aes(yintercept = 0), linetype = 'dashed', color = 'black')
  
  p3 <- ggplot(diff.mean,aes(var,estimate,fill = Group)) +
    geom_text(aes(y = 0,x = var),label = diff.mean$p.value,
              hjust = 0,fontface = "bold",inherit.aes = FALSE,size = 3) +
    geom_text(aes(x = nrow(diff.mean)/2 +0.5,y = 0.85),label = "P-value (corrected)",
              srt = 90,fontface = "bold",size = 5) +
    coord_flip() +
    ylim(c(0,1)) +
    theme(panel.background = element_blank(),
          panel.grid = element_blank(),
          axis.line = element_blank(),
          axis.ticks = element_blank(),
          axis.text = element_blank(),
          axis.title = element_blank())
  
  
  library(patchwork)
  p <- p1 + p2 + p3 + plot_layout(widths = c(4,6,2))
  p

  i = 1
# filename = paste(diffpath,"/",paste(allgroup[,i][1],allgroup[,i][2],sep = "_"),"stemp_P_plot.csv",sep = "")
# write.csv(diff.mean,filename)
filename = paste(diffpath,"/",paste(allgroup[,i][1],
                                        allgroup[,i][2],sep = "_"),phyloseq::rank.names(ps)[j],"stemp_plot.pdf",sep = "")
ggsave(filename,p,width = 14,height = 6)
    
filename = paste(diffpath,"/",paste(allgroup[,i][1],
                                        allgroup[,i][2],sep = "_"),phyloseq::rank.names(ps)[j],"stemp_plot.jpg",sep = "")
ggsave(filename,p,width = 14,height = 6)
detach("package:patchwork")

Heatmap + Bubble diagram (热图+气泡图)

ps = readRDS("./data/dataNEW/ps_16s.rds")

heatpath = paste(otupath,"/heapmap_boplot/",sep = "")
dir.create(heatpath)

map = phyloseq::sample_data(ps)
map$ID = row.names(map)
phyloseq::sample_data(ps) = map
j = 2
ps_tem = ps %>% 
    ggClusterNet::scale_micro(method = "TMM") %>%
    ggClusterNet::tax_glom_wt(ranks = j) 

rowSD = function(x){
    apply(x,1, sd)
  }
  
rowCV = function(x){
    rowSD(x)/rowMeans(x)
  }
  
id <- ps %>% 
    ggClusterNet::scale_micro(method = "TMM") %>%
    ggClusterNet::tax_glom_wt(ranks = j) %>%
    ggClusterNet::filter_OTU_ps(100) %>%
    ggClusterNet::vegan_otu() %>%
    t() %>% as.data.frame() %>%rowCV %>%
    sort(decreasing = TRUE) %>%
    head(20) %>%
    names()
  

ps_rela= ps_tem
heatnum = 20
label=TRUE
col_cluster=TRUE
row_cluster=TRUE


  map = phyloseq::sample_data(ps_rela)
  map$ID = row.names(map)
  phyloseq::sample_data(ps_rela) = map
  otu = as.data.frame(t(ggClusterNet::vegan_otu(ps_rela)))
  otu = as.matrix(otu[id,])
  ps_heatm = phyloseq::phyloseq(
    phyloseq::otu_table(otu,taxa_are_rows = TRUE),
    phyloseq::tax_table(ps_rela),
    phyloseq::sample_data(ps_rela)
    
  )
  
  # print(ps_heatm)
  datah <- as.data.frame(t(ggClusterNet::vegan_otu(ps_heatm)))
  head(datah) 
  tax = as.data.frame(ggClusterNet::vegan_tax(ps_heatm))
  
  otutaxh = cbind(datah,tax)
  head(otutaxh)
  
  otutaxh$id = paste(row.names(otutaxh),otutaxh$Genus,sep = "_")
  # otutaxh$id  = row.names(otutaxh)
  row.names(otutaxh) = otutaxh$id 
  
  
  data <- otutaxh[,c("id",phyloseq::sample_names(ps))]

  rig <- data[,phyloseq::sample_names(ps)] %>% rowMeans() %>% as.data.frame()
  head(rig)
  colnames(rig) = "MeanAbundance"
  rig$id = row.names(rig)
  rig = rig %>% dplyr::arrange(MeanAbundance)
  rig$id = factor(rig$id,levels = rig$id)
  p_rig = ggplot(rig) + geom_bar(aes(y = id,x = MeanAbundance),
                                 fill = "#A54657",
                                 stat = "identity") + theme_void()
  
  tem = data[,phyloseq::sample_names(ps)] %>% as.matrix()
  
   tem = scale(t(tem)) %>% t() %>% 
     as.data.frame()
   data[,phyloseq::sample_names(ps)] = tem
   
   
   
  # data[data > 0.3]<-0.3
  mat <- data[,-1] #drop gene column as now in rows
  
  if (col_cluster ==  TRUE) {
    clust <- hclust(dist(mat %>% as.matrix())) # hclust with distance matrix
    ggtree_plot <- ggtree::ggtree(clust)
  }
  if (row_cluster ==  TRUE) {
    v_clust <- hclust(dist(mat %>% as.matrix() %>% t()))
    ggtree_plot_col <- ggtree::ggtree(v_clust) + ggtree::layout_dendrogram()
  }
  
  if (label ==  TRUE) {
    map = as.data.frame(phyloseq::sample_data(ps))
    map$ID = row.names(map)
    labels= ggplot(map, aes(x = ID, y=1, fill=Group)) + geom_tile() +
      scale_fill_brewer(palette = 'Set1',name="Cell Type") +
      theme_void()
  }
  
  map = phyloseq::sample_data(ps) %>% as.tibble() %>%
    dplyr::arrange(Group) %>% as.data.frame()
  map$ID
#>  [1] "sample1"  "sample2"  "sample3"  "sample4"  "sample5"  "sample6" 
#>  [7] "sample10" "sample11" "sample12" "sample7"  "sample8"  "sample9" 
#> [13] "sample13" "sample14" "sample15" "sample16" "sample17" "sample18"
  pcm = reshape2::melt(data, id = c("id"))
  pcm$variable = factor(pcm$variable,levels = map$ID)
  pcm$id = factor(pcm$id,levels = rig$id)
  
  
  p1 = ggplot(pcm, aes(y = id, x = variable)) + 
    # geom_point(aes(size = value,fill = value), alpha = 0.75, shape = 21) + 
    geom_tile(aes(size = value,fill = value))+
    scale_size_continuous(limits = c(0.000001, 100), range = c(2,25), breaks = c(0.1,0.5,1)) + 
    labs( y= "", x = "", size = "Relative Abundance (%)", fill = "")  + 
    # scale_fill_manual(values = colours, guide = FALSE) + 
    scale_x_discrete(limits = rev(levels(pcm$variable)))  + 
    scale_y_discrete(position = "right") +
    scale_fill_gradientn(colours =colorRampPalette(RColorBrewer::brewer.pal(11,"Spectral")[11:1])(60))+
    theme(
      panel.background=element_blank(),
      panel.grid=element_blank(),
      axis.text.x = element_text(colour = "black",angle = 90)
      
    )
  
  colours = c( "#A54657",  "#582630", "#F7EE7F", "#4DAA57","#F1A66A","#F26157", "#F9ECCC", "#679289", "#33658A",
               "#F6AE2D","#86BBD8")
  #----样本在y轴上
  p2 = ggplot(pcm, aes(y = id, x = variable)) + 
    geom_point(aes(size = value,fill = value), alpha = 0.75, shape = 21) + 
    scale_size_continuous(limits = c(0.000001, 100), range = c(2,25), breaks = c(0.1,0.5,1)) + 
    labs( y= "", x = "", size = "Relative Abundance (%)", fill = "")  + 
    # scale_fill_manual(values = colours, guide = FALSE) + 
    scale_x_discrete(limits = rev(levels(pcm$variable)))  + 
    scale_y_discrete(position = "right")  +
    scale_fill_gradientn(colours =colorRampPalette(RColorBrewer::brewer.pal(11,"Spectral")[11:1])(60)) +
     theme(
      panel.background=element_blank(),
      panel.grid=element_blank(),
      axis.text.x = element_text(colour = "black",angle = 90)
      
      )

  p1 <- p1  %>%
    aplot::insert_right(p_rig, width=.2) 
  p2 <- p2  %>%
    aplot::insert_right(p_rig, width=.2) 
  if (col_cluster ==  TRUE) {
    p1 <- p1  %>%
      aplot::insert_left(ggtree_plot, width=.2)
    p2 <- p2  %>%
      aplot::insert_left(ggtree_plot, width=.2)
  }

  if (row_cluster ==  TRUE) {
    p1 <- p1  %>%
      aplot::insert_top(labels, height=.02) 
    p2 <- p2  %>%
      aplot::insert_top(labels, height=.02) 
  }
  
  if (label ==  TRUE) {
    p1 <- p1  %>%
      aplot::insert_top(ggtree_plot_col, height=.1)
    p2 <- p2  %>%
      aplot::insert_top(ggtree_plot_col, height=.1)
  }


  filename = paste(heatpath,"/",phyloseq::rank.names(ps)[j],"Topggheatmap.pdf",sep = "")
  ggsave(filename,p1,width = 14,height = (6 + heatnum/10))
  
  filename = paste(heatpath,phyloseq::rank.names(ps)[j],"Topggbubble.pdf",sep = "")
  ggsave(filename,p2,width = 14,height = (6 + heatnum/10))
  
  filename = paste(heatpath,"/",phyloseq::rank.names(ps)[j],"Topggheatmap.png",sep = "")
  ggsave(filename,p1,width = 14,height = (6 + heatnum/10))
  
  filename = paste(heatpath,phyloseq::rank.names(ps)[j],"Topggbubble.png",sep = "")
  ggsave(filename,p2,width = 14,height = (6 + heatnum/10))
  

Multi-group difference analysis volcano plot(多组差异分析火山图)

# BiocManager::install("MetBrewer")


diffpath.1 = paste(otupath,"/Mui.Group.v/",sep = "")
dir.create(diffpath.1)

source("./function/EdgerSuper2.R")
res = EdgerSuper2 (ps = ps,group  = "Group",artGroup =NULL,
                   j = "OTU",
                   path = diffpath.1
)
#> [1] "Group1" "Group2"
#> [1] "Group1" "Group3"
#> [1] "Group2" "Group3"

head(res)


  res$ID = row.names(res)
  datv = res 
  # for循环挑选每个cluster的top前5 gene symbol
  tm.g <- function(data){
    id = data$group %>% unique()
    
    for (i in 1:length(id)) {
      tem = filter(data,group==id[i],level != "nosig") %>% 
        distinct(ID,.keep_all = TRUE) %>% 
        top_n(5,abs(logFC))
      if (i == 1) {
        tem2 = tem
      } else {
        tem2 = rbind(tem2,tem)
      }
    }
    return(tem2)
  }
  
  top <- tm.g(datv)
  # 先画背景柱,根据数据log2FC的max值,min值来确定
  #根据数据中log2FC区间确定背景柱长度:
  
  head(datv)
  
  tem = datv %>% group_by(group) %>% summarise(max = max(logFC),min = min(logFC)) %>% as.data.frame()
  
  col1<-data.frame(x=tem$group,
                   y=tem$max)
  col2<-data.frame(x=tem$group,
                   y=tem$min)
  # 绘制背景柱
  p1 <- ggplot()+
    geom_col(data = col1,
             mapping = aes(x = x,y = y),
             fill = "#dcdcdc",alpha = 0.6)+
    geom_col(data = col2,
             mapping = aes(x = x,y = y),
             fill = "#dcdcdc",alpha = 0.6)
  p1

  
  
  
  #把散点火山图叠加到背景柱上:
  head(datv)
  
  p2 <- ggplot()+
    geom_col(data = col1,
             mapping = aes(x = x,y = y),
             fill = "#dcdcdc",alpha = 0.6)+
    geom_col(data = col2,
             mapping = aes(x = x,y = y),
             fill = "#dcdcdc",alpha = 0.6)+
    geom_jitter(data = datv,
                aes(x =group , y = logFC, color =level ),
                size = 1,
                width =0.4)+
    scale_color_manual(name=NULL,
                       values = c("#4393C3","#FC4E2A","grey40"))+
    labs(x="",y="log2(FoldChange)")
  p2

  
  # 添加X轴的分组色块标签:
  dfcol<-data.frame(x=tem$group,
                    y=0,
                    label=tem$group)
  # 添加分组色块标签
  dfcol$group <- tem$group
  # 加载包
  library(RColorBrewer)
  library(MetBrewer)
  # BiocManager::install("MetBrewer")
  # 自定义分组色块的颜色
  tile_color <- met.brewer("Thomas",length(tem$group))
  
  # 在图中镶嵌色块
  p3 <- p2 + geom_tile(data = dfcol,
                       aes(x=x,y=y),
                       height=1.75,
                       color = "black",
                       fill = tile_color,
                       alpha = 0.6,
                       show.legend = F)+
    geom_text(data=dfcol,
              aes(x=x,y=y,label=group),
              size =3.5,
              color ="white") + theme_classic()
  p3

  
  library(ggrepel)
  p4<-p3+geom_text_repel(
    data=top,
    aes(x=group,y=logFC,label=ID),
    force = 1.2,
    arrow = arrow(length = unit(0.008, "npc"),
                  type = "open", ends = "last"))
  p4

  # 去除背景,美化图片
  p5 <- p4+
    theme_minimal()+
    theme(
      axis.title = element_text(size = 18,
                                color = "black",
                                face = "bold"),
      axis.line.y = element_line(color = "black",
                                 size = 1.2),
      axis.line.x = element_blank(),
      axis.text.x = element_blank(),
      panel.grid = element_blank(),
      legend.position = "top",
      legend.direction = "vertical",
      legend.justification = c(1,0),
      legend.text = element_text(size = 12)
    )
  p5


# return(list(p5,p3,datv,top))
p = p3
p

filename = paste(diffpath.1,"/","Mui.group.volcano.pdf",sep = "")
ggsave(filename,p,width = 12,height = 6,limitsize = FALSE)

3.Biomarker identification (3.生物标志物鉴别)

LEfSe analysis(LEfSe分析)


lefsepath = paste(otupath,"/lefse_R_plot/",sep = "")
dir.create(lefsepath)

ps = readRDS("./data/dataNEW/ps_16s.rds")
library(ggtree)

p_base = function(ps,Top = 100,ranks = 6) {
  alltax = ps %>%
    ggClusterNet::tax_glom_wt(ranks = ranks ) %>%
    ggClusterNet::filter_OTU_ps(Top) %>%
    ggClusterNet::vegan_tax() %>%
    as.data.frame()
  alltax$OTU = row.names(alltax)
  alltax$Kingdom = paste(alltax$Kingdom,sep = "_Rank_")
  for (i in 2:ranks) {
    alltax[,i]= paste(alltax[,i - 1],alltax[,i],sep = "_Rank_")
    
  }
  
  alltax[is.na(alltax)] = "Unknown"
  trda <- MicrobiotaProcess::convert_to_treedata(alltax)
  
  p <- ggtree(trda, layout="circular", size=0.2, xlim=c(30,NA)) +
    geom_point(
      pch = 21,
      size=3,
      alpha=1,
      fill = "#FFFFB3"
    )
  p$data$lab2 <- p$data$label %>% strsplit( "_Rank_") %>%
    sapply(
      function(x) x[length(x)]
    )
  p$data$lab2   = gsub("st__","",p$data$lab2 )
  p$data$nodeSize = 1
  return(p)
}


LDA_Micro = function(ps = ps,
                     Top = 100,
                     ranks = 6,
                     p.lvl = 0.05,
                     lda.lvl = 2,
                     seed = 11,
                     adjust.p = F
){
  
  
  alltax = ps %>%
    ggClusterNet::tax_glom_wt(ranks = ranks ) %>%
    phyloseq::filter_taxa(function(x) sum(x ) > 0 , TRUE) %>%
    ggClusterNet::filter_OTU_ps(Top) %>%
    ggClusterNet::vegan_tax() %>%
    as.data.frame()
  alltax$OTU = row.names(alltax)
  

  
  alltax$Kingdom = paste(alltax$Kingdom,sep = "_Rank_")
  for (i in 2:ranks) {
    alltax[,i]= paste(alltax[,i - 1],alltax[,i],sep = "_Rank_")
    
  }
  
  otu = ps %>%
    ggClusterNet::tax_glom_wt(ranks = ranks ) %>%
    phyloseq::filter_taxa(function(x) sum(x ) > 0 , TRUE) %>%
    ggClusterNet::filter_OTU_ps(Top) %>%
    ggClusterNet::vegan_otu() %>%
    t() %>%
    as.data.frame()
  
  otu_tax = merge(otu,alltax,by = "row.names",all = F)
  head(otu_tax)
  
  tem = colnames(alltax)[-length(colnames(alltax))]
  i = 1
  tem2 = c("k__","p__","c__","o__","f__","g__","s__","st__")
  for (i in 1:ranks) {
    rank1 <- otu_tax %>%
      dplyr::group_by(!!sym(tem[i])) %>%
      dplyr::summarise_if(is.numeric, sum, na.rm = TRUE)
    colnames(rank1)[1] = "id"
    rank1$id = paste(tem2[i],rank1$id,sep = "")
    if (i == 1) {
      all = rank1
    }
    
    if (i != 1) {
      all = rbind(all,rank1)
    }
  }
  
  
  #--LDA排序#--------
  data1 = as.data.frame(all)
  row.names(data1) = data1$id
  data1$id = NULL
  
  #-构建phylose对象
 
  ps_G_graphlan = phyloseq::phyloseq(phyloseq::otu_table(as.matrix(data1),taxa_are_rows = TRUE), 
                                     phyloseq::sample_data(ps))#   %>% filter_taxa(function(x) sum(x ) > 0 , TRUE)
  ps_G_graphlan 

  #----提取OTU表格
  
  otu = as.data.frame((ggClusterNet::vegan_otu(ps_G_graphlan)))

  
  map = as.data.frame(phyloseq::sample_data(ps_G_graphlan))
  # otu = (otu_table)
  claslbl= map$Group %>% as.factor()
  set.seed(seed)
  #KW rank sum test
  
  rawpvalues <- apply(otu, 2, function(x) kruskal.test(x, claslbl)$p.value);
  #--得到计算后得到的p值
  ord.inx <- order(rawpvalues)
  rawpvalues <- rawpvalues[ord.inx]
  clapvalues <- p.adjust(rawpvalues, method ="fdr")
  # p.adjust
  wil_datadf <- as.data.frame(otu[,ord.inx])

  ldares <- MASS::lda(claslbl ~ .,data = wil_datadf)
  # ldares
  ldamean <- as.data.frame(t(ldares$means))
  ldamean 
  class_no <<- length(unique(claslbl))
  ldamean$max <- apply(ldamean[,1:class_no],1,max);
  ldamean$min <- apply(ldamean[,1:class_no],1,min);
  #---计算LDA
  ldamean$LDAscore <- signif(log10(1+abs(ldamean$max-ldamean$min)/2),digits=3);
  head(ldamean)
  
  a = rep("A",length(ldamean$max))
  for (i in 1:length(ldamean$max)) {
    name =colnames(ldamean[,1:class_no])
    a[i] = name[ldamean[,1:class_no][i,] %in% ldamean$max[i]]
  }
  ldamean$class = a
  
  tem1 = row.names(ldamean)
  tem1 %>% as.character()
  ldamean$Pvalues <- signif(rawpvalues[match(row.names(ldamean),names(rawpvalues))],digits=5)
  ldamean$FDR <- signif(clapvalues,digits=5)
  resTable <- ldamean
  rawNms <- rownames(resTable);
  rownames(resTable) <- gsub("`", '', rawNms);
  
  
  if (adjust.p) {
    de.Num <- sum(clapvalues <= p.lvl & ldamean$LDAscore>=lda.lvl)
    
  } else {
    de.Num <- sum(rawpvalues <= p.lvl & ldamean$LDAscore>=lda.lvl)
  }
  
  if(de.Num == 0){
    current.msg <<- "No significant features were identified with given criteria.";
  }else{
    current.msg <<- paste("A total of", de.Num, "significant features with given criteria.")
  }
  print(current.msg)
  # sort by p value
  ord.inx <- order(resTable$Pvalues, resTable$LDAscore)
  resTable <- resTable[ord.inx, ,drop=FALSE]
  resTable <- resTable[,c(ncol(resTable),1:(ncol(resTable)-1))]
  resTable <- resTable[,c(ncol(resTable),1:(ncol(resTable)-1))]
  ldamean$Pvalues[is.na(ldamean$Pvalues)] = 1
  # resTable %>% tail()
  if (adjust.p) {
    taxtree = resTable[clapvalues <=p.lvl & ldamean$LDAscore>=lda.lvl,]
  } else {
    # taxtree = resTable[ldamean$Pvalues <=p.lvl & ldamean$LDAscore>=lda.lvl,]
    taxtree = resTable[ldamean$Pvalues <=p.lvl,]
  }
  
  #-提取所需要的颜色
  colour = c('darkgreen','red',"blue","#4DAF4A", "#984EA3", "#FF7F00", "#FFFF33", "#A65628", "#F781BF")
  selececol = colour[1:length(levels(as.factor(taxtree$class)))]
  names(selececol) = levels(as.factor(taxtree$class))
  A = rep("a",length(row.names(taxtree)))
  
  for (i in 1:length(row.names(taxtree))) {
    A[i] = selececol [taxtree$class[i]]
  }
  
  taxtree$color = A
  # taxtree <- taxtree[row.names(taxtree) != "k__Bacteria",]
  # node_ids <- p0$data  
  # anno <- rep("white", nrow(p1$data))
  
  lefse_lists = data.frame(node=row.names(taxtree),
                           color=A,
                           Group = taxtree$class,
                           stringsAsFactors = FALSE
  )
  
  
  return(list(lefse_lists,taxtree))
}


# gtree = p1
# anno.data= tablda[[1]]
# alpha=0.3
# anno.depth = 2
library(patchwork)

clade.anno_wt <- function(gtree, anno.data, alpha = 0.2, anno.depth = 5, anno.x = 10, 
                          anno.y = 40){
  short.labs <- c(letters,paste(letters,1:500,sep = ""))
  get_offset <- function(x) {
    (x * 0.2 + 0.2)^2
  }
  get_angle <- function(node) {
    data <- gtree$data
    sp <- tidytree::offspring(data, node)$node
    sp2 <- c(sp, node)
    sp.df <- data[match(sp2, data$node), ]
    mean(range(sp.df$angle))
  }
  
  
  anno.data <- dplyr::arrange(anno.data, node)
  hilight.color <- anno.data$color
  node_list <- anno.data$node
  node_ids <- (gtree$data %>% filter(label %in% node_list) %>% 
                 arrange(label))$node
  anno <- rep("yellow", nrow(gtree$data))
  
  #---添加阴影#-------
  i = 1
  for (i in 1:length(node_ids)) {
    n <- node_ids[i]
    color <- hilight.color[i]
    anno[n] <- color
    mapping <- gtree$data %>% filter(node == n)
    nodeClass <- as.numeric(mapping$nodeDepth)
    offset <- get_offset(nodeClass)
    gtree <- gtree + geom_hilight(node = n, fill = color, 
                                  alpha = alpha, extend = offset)
  }
  # gtree$layers <- rev(gtree$layers)
  # gtree <- gtree + geom_point2(aes(size = I(nodeSize)), fill = anno, 
  #                              shape = 21)
  short.labs.anno <- NULL
  i = 1
  # gtree$data
  
  
  #--添加标签#--------
  for (i in 1:length(node_ids)) {
    n <- node_ids[i]
    mapping <- gtree$data %>% filter(node == n)
    nodeClass <- as.numeric(mapping$nodeDepth)
    if (nodeClass <= anno.depth) {
      lab <- short.labs[1]
      short.labs <- short.labs[-1]
      if (is.null(short.labs.anno)) {
        short.labs.anno = data.frame(lab = lab, annot = mapping$lab2, 
                                     stringsAsFactors = F)
      }else {
        short.labs.anno = rbind(short.labs.anno, c(lab,mapping$lab2))
      }
    } else {
      lab <- mapping$lab2
    }
    
    offset <- get_offset(nodeClass) - 0.4
    angle <- get_angle(n) + 90
    gtree <- gtree + geom_cladelabel(node = n, label = lab, 
                                     
                                     angle = angle, fontsize = 1 + sqrt(nodeClass), 
                                     offset = offset, barsize = NA, hjust = 0.5)
  }
  
  if (!is.null(short.labs.anno)) {
    anno_shapes = sapply(short.labs.anno$lab, utf8ToInt)
    stable.p <- ggpubr::ggtexttable(short.labs.anno, rows = NULL, 
                                    theme = ggpubr::ttheme(
                                      colnames.style = ggpubr::colnames_style(fill = "white"),
                                      tbody.style = ggpubr::tbody_style(fill = ggpubr::get_palette("RdBu", 6))
                                    ))
    
  } 
  
  
  y = (1:length(unique(anno.data$Group)))
  
  pleg <- ggplot() + geom_point2(aes(
    y = y,
    x = rep(1,length(unique(anno.data$color))),fill = as.factor(1:length(unique(anno.data$Group)))
  ),pch = 21,size = 2) +
    geom_text(aes(  y = y,
                    x = rep(1,length(unique(anno.data$color))),label = unique(anno.data$Group) ),
              hjust = -1
    ) + scale_fill_manual(values = unique(anno.data$color),guide = F) +
    theme_void()
  layout <- "
  AAAAAABB#
  AAAAAABB#
  AAAAAABBC
  AAAAAABBC
  AAAAAABB#
  "
  
  
  if (is.null(short.labs.anno)) {
    gtree <- gtree + pleg + plot_layout(design = layout)
  } else {
    gtree <- gtree + stable.p+ pleg + plot_layout(design = layout)
  }
  
}

lefse_bar = function(taxtree = tablda[[2]]){
  taxtree = tablda[[2]]
  taxtree$ID = row.names(taxtree)
  head(taxtree)
  taxtree$ID = gsub("_Rank_",";",taxtree$ID)
  taxtree <- taxtree %>%
    arrange(class,LDAscore)
  taxtree$ID = factor(taxtree$ID,levels=taxtree$ID)
  taxtree$class = factor(taxtree$class,levels = unique(taxtree$class))
  
  pbar <- ggplot(taxtree) + geom_bar(aes(y =ID, x = LDAscore,fill = class),stat = "identity") +
    scale_fill_manual(values = unique(taxtree$color)) + mytheme1 +
    scale_x_continuous(limits = c(0,max(taxtree$LDAscore)*1.2))
}


for (j in 2:4) {
  
  p1 <- p_base(ps,Top = 200,ranks =j)
  p1
  
  tablda = LDA_Micro(ps = ps,
                     Top = 200,
                     ranks = j,
                     p.lvl = 0.05,
                     lda.lvl = 2,
                     seed = 11,
                     adjust.p = F)
  
  p2 <- clade.anno_wt(p1, tablda[[1]], alpha=0.3,anno.depth = 2)
  p2
  FileName <- paste(lefsepath,j,"_tree_lefse", ".pdf", sep = "")
  ggsave(FileName,p2,width = 15,height = 10)
  FileName <- paste(lefsepath,j,"_tree_lefse", ".png", sep = "")
  ggsave(FileName,p2,width = 15,height = 10)
  p <- lefse_bar(taxtree = tablda[[2]])
  FileName <- paste(lefsepath,j,"_bar_lefse", ".pdf", sep = "")
  ggsave(FileName, p, width = 15, height =9)
  
  FileName <- paste(lefsepath,j,"_bar_lefse", ".png", sep = "")
  ggsave(FileName, p, width = 15, height =9)
  
  res = tablda[[2]]
  FileName <- paste(lefsepath,j,"_tree_lefse_data", ".csv", sep = "")
  write.csv(res,FileName,quote = F)
}
#> [1] "A total of 5 significant features with given criteria."
#> [1] "A total of 14 significant features with given criteria."
#> [1] "A total of 22 significant features with given criteria."

Machine learning(机器学习)

Microbial communities + Science article- Chart combination(微生物群落和环境Science组合图表)

ps = base::readRDS("./data/dataNEW/ps_16s.rds")
env = read.csv("./data/dataNEW/env.csv")
head(env)
envRDA = env
head(env)
row.names(envRDA) = env$ID
envRDA$ID = NULL
head(envRDA)

compath = paste(res1path,"/Conbine_env_plot/",sep = "")
dir.create(compath)

otu1 = as.data.frame(t(ggClusterNet::vegan_otu(ps)))
head(otu1)

tabOTU1 = list(bac = otu1)

# tabOTU1 = list(b = otu)
# tabOTU1 = list(f = otu2)

p0 <- ggClusterNet:: MatCorPlot(env.dat = envRDA,
                 tabOTU = tabOTU1,
                 diag = FALSE,
                 range = 0.1,
                 numpoint = 21,
                 sig = TRUE,
                 siglabel = FALSE,
                 shownum = FALSE,
                 curvature = 0.1,
                 numsymbol = NULL,
                 lacx = "left",
                 lacy = "bottom",
                 p.thur = 0.05,
                 onlysig = TRUE
)
p0


p0 <- p0 + scale_colour_manual(values = c("blue","red")) + 
  scale_fill_distiller(palette="PRGn")
p0


FileName <- paste(compath,"Conbine_envplot", ".pdf", sep = "")
ggsave(FileName, p0,width = 15,height = 10)# , device = cairo_pdf, family = "Song"

FileName <- paste(compath,"Conbine_envplot", ".png", sep = "")
ggsave(FileName, p0,width = 15,height = 10)# , device = cairo_pdf, family = "Song"



#--拆分开来
#--- mantel test
rep = ggClusterNet::MetalTast (env.dat = envRDA, tabOTU = tabOTU1,distance = "bray",method = "metal")
repR = rep[c(-seq(from=1,to=dim(rep)[2],by=2)[-1])]
repP = rep[seq(from=1,to=dim(rep)[2],by=2)]
head( repR)
head( repP)
mat = cbind(repR,repP)
head(mat)

FileName <- paste(compath,"Conbine_envplot_data", ".csv", sep = "")
write.csv(mat,FileName)


result <- ggClusterNet::Miccorplot(data = envRDA,
                     method.cor = "spearman",
                     cor.p = 0.05,
                     x = F,
                     y = F,
                     diag = T,
                     lacx = "left",
                     lacy = "bottom",
                     sig = T,
                     siglabel = F,
                     shownum = F,
                     numpoint = 21,
                     numsymbol = NULL
)

p1 <- result[[1]]
p1 <- p1 + scale_fill_distiller(palette="PRGn")
p1

FileName <- paste(compath,"envCorplot", ".pdf", sep = "")
ggsave(FileName, p1,width = 15,height = 10)
FileName <- paste(compath,"envCorplot", ".png", sep = "")
ggsave(FileName, p1,width = 15,height = 10)